An Aggrephagy-Related LncRNA Signature for the Prognosis of Pancreatic Adenocarcinoma

TSPAN4+ fibroblasts coordinate metastatic niche assembly through migrasome-driven metabolic reprogramming and stromal-immune crosstalk in pancreatic adenocarcinoma

Background

Pancreatic cancer (PC) is a highly aggressive pancreatic malignant tumor with poor prognosis due to its complex tumor microenvironment (TME) and metastatic potential. Fibroblasts play an important role in tumor progression and metastasis by remodeling the extracellular matrix (ECM) and influencing the immune response. This study explored migrasome-associated fibroblasts, especially TSPAN4 and ITGA5, as key regulators of pancreatic cancer metastasis, aiming to provide new ideas for therapeutic strategies targeting TME.

Methods

We employed single-cell RNA sequencing (scRNA-seq) and spatial transcriptomics to analyze pancreatic cancer tissues. Data from the GEO and TCGA databases were integrated and processed using batch correction techniques. Single-cell data were analyzed with Seurat and Monocle for dimensionality reduction and pseudotime trajectory analysis. Cell communication was assessed using CellCall and CellChat. Spatial transcriptomic analysis was conducted with RCTD and MISTy tools to investigate cell interactions within the TME. Additionally, gene enrichment, deconvolution, and prognostic analyses were performed, alongside experimental validation through siRNA transfection, qRT-PCR, and various functional assays to investigate the role of TSPAN4 in metastasis.

Results

Our results underscore the critical role of TSPAN4+ fibroblasts in pancreatic cancer. These fibroblasts were predominantly located at the tumor periphery and exhibited elevated migrasome gene expression, which was associated with enhanced ECM remodeling and immune suppression. Intercellular communication analysis revealed that TSPAN4+ fibroblasts engaged in extensive interactions with immune cells, such as macrophages and endothelial cells, facilitating metastasis and immune evasion. Moreover, the high expression of immune checkpoint markers indicated their involvement in modulating the immune response.

Conclusion

TSPAN4+ fibroblasts are key regulators of pancreatic cancer progression, contributing to metastasis, immune suppression, and ECM remodeling. Targeting these fibroblasts represents a promising therapeutic strategy to improve clinical outcomes and enhance the effectiveness of immunotherapies in pancreatic cancer.

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.

The aggrephagy-related gene TUBA1B influences clinical outcomes in glioma patients by regulating the cell cycle

Background

Gliomas are common primary malignant brain tumors, with glioblastoma (GBM) being the most aggressive subtype. GBM is characterized by high recurrence rates and treatment resistance, leading to poor patient outcomes. Current prognostic models have limited predictive power, underscoring the need to elucidate underlying mechanisms and identify novel biomarkers to improve therapeutic strategies and prognostic models.

Methods

Gene expression and clinical data for GBM and LGG were obtained from the TCGA and CGGA database, while single-cell sequencing data from GSE167960 were selected from the GEO database. Molecular characteristics of gliomas were revealed through normalization, consensus clustering analysis, immune scoring, cell infiltration analysis, and pathway analysis. TUBA1B, identified as a key gene through machine learning, was incorporated into a nomogram model using multivariate Cox regression. Its functions were validated through qRT-PCR, in vitro functional assays, and mouse xenograft models. All data analyses and statistics were performed using R software.

Results

Consensus clustering of the TCGA glioma dataset identified two aggrephagy subtypes (C1 and C2), with C2 showing worse survival outcomes and higher immune infiltration. TUBA1B was identified as an independent prognostic marker, with high expression associated with upregulated cell cycle pathways and alterations in the immune microenvironment. TUBA1B was shown to influence glioma cell proliferation, migration, invasion, and autophagy, impacting tumor progression and treatment response through intercellular communication and metabolic pathways.

Conclusion

The study demonstrates that high TUBA1B expression is closely associated with glioma malignancy and poor prognosis, making it a potential therapeutic target.

Single-cell RNA sequencing revealed PPARG promoted osteosarcoma progression: based on osteoclast proliferation

Background

Osteosarcoma (OS) is one of the most common primary malignant bone tumors, primarily originating from mesenchymal tissue. It is notorious for its high invasiveness, high disability rate, high mortality rate, and poor prognosis. In most primary and metastatic malignant tumors, bone destruction can promote cancer progression, which is closely related to osteoclast activation and the imbalance between osteoblasts and osteoclasts. A large number of studies confirmed that osteoclasts are an important part of OS, which play an active role in destroying bone homeostasis and promoting the progress of OS. Therefore, we conducted a detailed study of osteoclasts at the single cell level, aiming to find new OS therapeutic targets to prevent tumor progression and local spread.

Methods

We analyzed the single-cell sequencing data of OS patients and usedMonocle2, Cytotrace, and Slingshot software to analyze the pseudo-sequential trajectory during OS progression. CellChat was used to reveal the communication between cells. PySCENIC was used to identify active transcription factors in osteoclasts. Finally, we further demonstrated the results by RT-qPCR analysis, CCK-8 assay, wound healing assay, Transwell assay, etc.

Results

Through the analysis of single-cell sequencing data in OS, we identified a highly specific subgroup, C2MKI67+ Osteoclast. The key signaling pathway APP and the top 1 transcription factor PPARG in this subgroup played essential roles in osteoclast proliferation and differentiation. Given the pivotal role of osteoclasts in OS progression, we speculated that these signaling pathways and transcription factors could emerge as novel therapeutic targets, offering innovative strategies for OS treatment.

Conclusion

This study enhanced our understanding of OS and osteoclasts through scRNA-seq. Furthermore, we discovered that PPARG amplifies osteoclast activation and proliferation, resulting in excessive bone resorption and degradation of the bone matrix, thereby creating a favorable environment for tumor cell proliferation and growth. By innovatively targeting PPARG, it affected osteoclast proliferation and thus affected tumor progression; this work offered new insights and directions for the clinical treatment of OS patients.

Integrative Analysis of Whole-Genome and Transcriptomic Data Reveals Novel Variants in Differentially Expressed Long Noncoding RNAs Associated with Asthenozoospermia

Background/Objectives: Asthenozoospermia, characterized by reduced sperm motility, is a common cause of male infertility. Emerging evidence suggests that noncoding RNAs, particularly long noncoding RNAs (lncRNAs), play a critical role in the regulation of spermatogenesis and sperm function. Coding regions have a well-characterized role and established predictive value in asthenozoospermia. However, this study was designed to complement previous findings and provide a more holistic understanding of asthenozoospermia, this time focusing on noncoding regions. This study aimed to identify and prioritize variants in differentially expressed (DE) lncRNAs found exclusively in asthenozoospermic men, focusing on their impact on lncRNA structure and lncRNA-miRNA-mRNA interactions. Methods: Whole-genome sequencing (WGS) was performed on samples from asthenozoospermic and normozoospermic men. Additionally, an RNA-seq dataset from normozoospermic and asthenozoospermic individuals was analyzed to identify DE lncRNAs. Bioinformatics analyses were conducted to map unique variants on DE lncRNAs, followed by prioritization based on predicted functional impact. The structural impact of the variants and their effects on lncRNA-miRNA interactions were assessed using computational tools. Gene ontology (GO) and KEGG pathway analyses were employed to investigate the affected biological processes and pathways. Results: We identified 4173 unique variants mapped to 258 DE lncRNAs. After prioritization, 5 unique variants in 5 lncRNAs were found to affect lncRNA structure, while 20 variants in 17 lncRNAs were predicted to disrupt miRNA-lncRNA interactions. Enriched pathways included Wnt signaling, phosphatase binding, and cell proliferation, all previously implicated in reproductive health. Conclusions: This study identifies specific variants in DE lncRNAs that may play a role in asthenozoospermia. Given the limited research utilizing WGS to explore the role of noncoding RNAs in male infertility, our findings provide valuable insights and a foundation for future studies.

Integrated single-cell RNA sequencing reveals the tumor heterogeneity and microenvironment landscape during liver metastasis in adenocarcinoma of esophagogastric junction

Background

Adenocarcinoma of the esophagogastric junction (AEGJ) is a highly aggressive tumor that frequently metastasizes to the liver. Understanding the cellular and molecular mechanisms that drive this process is essential for developing effective therapies.

Methods

We employed single-cell RNA sequencing to analyze the tumor heterogeneity and microenvironmental landscape in patients with AEGJ liver metastases. This approach enabled us to characterize the diverse cell populations involved in the liver metastatic process.

Results

Our analysis revealed a significant involvement of fibroblasts and mural cells in AEGJ liver metastasis. We identified a specific fibroblast type in AEGJ liver metastasis and observed distinct gene expression patterns between adenocarcinoma of the esophagogastric junction and other stomach adenocarcinomas. Our study demonstrated high expression of the SFRP2 gene in pericyte cells during the liver metastasis of AEGJ. The incorporation of GEO, TCGA, and immunofluorescence staining of SFRP2 expression enhanced our study. High expression of SFRP2 in pericytes may influence vascular stability and angiogenesis through the Wnt pathway.

Conclusion

Our study provides novel insights into the cellular interactions and molecular mechanisms that underlie AEGJ liver metastasis. Targeting the identified subtype of fibroblasts or influencing SFRP2 gene expression in pericytes may offer new therapeutic strategies for combating this aggressive tumor.

CXC chemokine receptor 4 - mediated immune modulation and tumor microenvironment heterogeneity in gastric cancer: Utilizing multi-omics approaches to identify potential therapeutic targets

G-protein-coupled receptors (GPRs) are critical regulators of various biological behaviors, and their role in gastric cancer (GC) progression is gaining increasing attention. Among them, the immune regulatory mechanisms mediated by chemokine receptor 4 (CXCR4) remain insufficiently understood. This study aims to explore the immune regulatory functions of CXCR4 and the heterogeneity of the tumor microenvironment (TME) by examining GPR-related gene expression in GC. Through multi-omics approaches, including spatial transcriptomics and single-cell RNA sequencing, we investigated the oncogenic mechanisms of CXCR4, particularly its role in T cell immune exhaustion. In vitro experiments, including ELISA, PCR, CCK8 assays, cell scratch assays, and colony formation assays, were used to validate the role of CXCR4 in the migration and invasion of AGS and SNU-1 cell lines. CXCR4 silencing using siRNA further demonstrated its regulatory effects on these cellular processes. Our results revealed a strong correlation between elevated CXCR4 expression and increased exhaustion of regulatory T cells (Tregs) in the TME. Furthermore, heightened CXCR4 expression was linked to increased TME heterogeneity, driven by oxidative stress and activation of the NF-κB pathway, promoting immune evasion and tumor progression. Silencing CXCR4 significantly inhibited the invasive and proliferative abilities of AGS and SNU-1 cells, while also reducing the expression of pro-inflammatory cytokines IL-1β and interleukin-6, thus alleviating chronic inflammation and improving TME conditions. In conclusion, our comprehensive investigation highlights CXCR4 as a key mediator of TME dynamics and immune modulation in GC. Targeting CXCR4 presents a promising therapeutic strategy to slow tumor progression by reducing Tregs-mediated immune exhaustion and TME heterogeneity, positioning it as a novel therapeutic target in GC treatment.

Exploring the role of Disulfidptosis in glioma progression: insights into tumor heterogeneity and therapeutic potential through single-cell RNA sequencing

BACKGROUND

Gliomas, particularly glioblastoma (GBM), are the most common and aggressive primary brain tumors in adults, characterized by high malignancy and frequent recurrence. Despite standard treatments, including surgery, radiotherapy, and chemotherapy, the prognosis for GBM remains poor, with a median survival of less than 15 months and a five-year survival rate below 10%. Tumor heterogeneity and resistance to treatment create significant challenges in controlling glioma progression. Therefore, there is an urgent need for new therapeutic targets and strategies.

OBJECTIVE

This study investigates the role of Disulfidptosis, a recently discovered form of programmed cell death, in gliomas. Unlike apoptosis and necrosis, Disulfidptosis is driven by the abnormal accumulation of intracellular disulfide bonds, leading to protein misfolding and cytoskeletal collapse, particularly in cancer cells with metabolic dysregulation. We aim to explore how glioma cells respond to Disulfidptosis and identify potential therapeutic targets by analyzing the heterogeneity of gliomas at the single-cell level using single-cell RNA sequencing (scRNA-seq).

METHODS

scRNA-seq data from glioma patients were analyzed to uncover differences in ferroptosis-related pathways, including iron metabolism and lipid peroxidation. Cellular subpopulations within gliomas were profiled to assess their sensitivity to Disulfidptosis and the underlying mechanisms. Survival analysis was conducted to evaluate the clinical relevance of Disulfidptosis-related gene expression.

RESULTS

Multiple cell subpopulations within gliomas exhibit varying sensitivities to Disulfidptosis, influenced by their metabolic properties. Dysregulated iron metabolism and antioxidant mechanisms were identified as key factors impacting Disulfidptosis sensitivity. Glioma microenvironment signaling pathways also play a role in regulating Disulfidptosis. These findings suggest that activating Disulfidptosis pathways may provide novel therapeutic strategies to overcome treatment resistance in gliomas.

CONCLUSION

This study offers new insights into the role of Disulfidptosis in glioma progression and highlights its potential as a therapeutic target. By leveraging single-cell sequencing data, the research uncovers tumor heterogeneity and identifies specific cell populations resistant to Disulfidptosis. These findings may pave the way for personalized treatment strategies to improve survival outcomes in glioma patients.

Polysaccharides targeting autophagy to alleviate metabolic syndrome

Metabolic syndrome is a prevalent non-communicable disease characterized by central obesity, insulin resistance, hypertension, hyperglycemia, and hyperlipidemia. Epidemiological statistics indicate that one-third of the world's population is affected by metabolic syndrome. Unfortunately, owing to complicated pathogenesis and limited pharmacological options, the growing prevalence of metabolic syndrome threatens human health worldwide. Autophagy is an intracellular degradation mechanism that involves the degradation of unfolded or aggregated proteins and damaged cellular organelles, thereby maintaining metabolic homeostasis. Increasing evidence indicates that dysfunctional autophagy is closely associated with the development of metabolic syndrome, making it an attractive therapeutic target. Furthermore, a growing number of plant-derived polysaccharides have been shown to regulate autophagy, thereby alleviating metabolic syndrome, such as Astragalus polysaccharides, Laminaria japonica polysaccharides, Ganoderma lucidum polysaccharides and Lycium barbarum polysaccharides. In this review, we summarize recent advances in the discovery of autophagy modulators of plant polysaccharides for the treatment of metabolic syndrome, with the aim of providing precursor compounds for the development of new therapeutic agents. Additionally, we look forward to seeing more diseases being treated with plant polysaccharides by regulating autophagy, as well as the discovery of more intricate mechanisms that govern autophagy.

Oxidative Stress and Cancer Therapy: Controlling Cancer Cells Using Reactive Oxygen Species

Cancer is a multifaceted disease influenced by various mechanisms, including the generation of reactive oxygen species (ROS), which have a paradoxical role in both promoting cancer progression and serving as targets for therapeutic interventions. At low concentrations, ROS serve as signaling agents that enhance cancer cell proliferation, migration, and resistance to drugs. However, at elevated levels, ROS induce oxidative stress, causing damage to biomolecules and leading to cell death. Cancer cells have developed mechanisms to manage ROS levels, including activating pathways such as NRF2, NF-κB, and PI3K/Akt. This review explores the relationship between ROS and cancer, focusing on cell death mechanisms like apoptosis, ferroptosis, and autophagy, highlighting the potential therapeutic strategies that exploit ROS to target cancer cells.

Multi-omics analysis reveals the role of the autophagy-related gene AGT in chemotherapy resistance in colorectal cancer and the therapeutic potential of its inhibitors

BACKGROUND

Autophagy is a crucial mechanism for maintaining cellular homeostasis and responding to environmental stress, and it is closely linked to tumor drug resistance. Through multi-omics analysis, this study explores the expression patterns, functions, and potential role of the autophagy-related gene Angiotensinogen (AGT) in colorectal cancer (CRC), particularly in relation to chemotherapy resistance.

METHODS

This study first compared AGT expression between CRC and normal tissues using the GTEx and TCGA databases. Differences in expression were assessed using Wilcoxon Rank Sum Tests, and the prognostic impact of AGT was evaluated through univariate Cox survival analysis and meta-analysis. Functional enrichment was performed using the limma and fgsea packages. Drug sensitivity analysis was conducted based on the CTRP database, while immune infiltration was assessed using the CIBERSORT and ESTIMATE methods. Spatial transcriptomic characteristics were explored through 10x Visium technology and deconvolution analysis to investigate the correlation between AGT expression levels and tumor cell content.scRNA-seq data from CRC tissues were sourced from Tumor Immune Single Cell Hub (TISCH).Functional annotation was performed with Single-sample gene set enrichment analysis (SSGSEA), and pseudotime analysis using Monocle 2 mapped their developmental trajectories. The potential of AGT inhibitors in the treatment of CRC was analyzed using drug-target Mendelian randomization.Finally, Phenome-Wide Association Study (PheWAS) was conducted to evaluate genetic associations and potential side effects of AGT inhibitors.

RESULTS

AGT expression was significantly higher in CRC tissues compared to normal tissues and was associated with shorter recurrence-free survival (RFS). Autophagy signaling pathways were markedly enriched in the high AGT expression group. AGT expression was positively correlated with resistance to chemotherapeutic agents such as gemcitabine, cisplatin, paclitaxel, and 5-fluorouracil. Spatial transcriptomic analysis revealed that AGT was predominantly expressed in malignant tumor regions. Single-cell analysis identified 21 distinct cell subpopulations across 13 major types. AGT expression was significantly higher in tumor samples, especially in the fibroblast C6 subpopulation. Tumor-related pathways were enriched in C1, C5, C6, and C8 subpopulations. Pseudotime analysis revealed that these subpopulations, particularly C6, were in terminal developmental stages.Drug-target Mendelian randomization analysis indicated a negative causal relationship between AGT inhibitors and the risk of both heart failure(ORdrug = 0.950, 95% CI, 0.912-0.990; P = 0.014) and CRC(ORdrug = 0.874, 95% CI: 0.792-0.964; P = 0.007).PheWAS analysis showed no genetic associations between AGT inhibitors and other traits, indicating its specificity and low risk of side effects.

CONCLUSION

Elevated AGT expression in CRC is associated with resistance to chemotherapy, and its inhibition may offer a therapeutic avenue for the treatment of colorectal cancer.

Integrated bulk and single-cell profiling characterize sphingolipid metabolism in pancreatic cancer

BACKGROUND

Abnormal sphingolipid metabolism (SM) is closely linked to the incidence of cancers. However, the role of SM in pancreatic cancer (PC) remains unclear. This study aims to explore the significance of SM in the prognosis, immune microenvironment, and treatment of PC.

METHODS

Single-cell and bulk transcriptome data of PC were acquired via TCGA and GEO databases. SM-related genes (SMRGs) were obtained via MSigDB database. Consensus clustering was utilized to construct SM-related molecular subtypes. LASSO and Cox regression were utilized to build SM-related prognostic signature. ESTIMATE and CIBERSORT algorithms were employed to assess the tumour immune microenvironment. OncoPredict package was used to predict drug sensitivity. CCK-8, scratch, and transwell experiments were performed to analyze the function of ANKRD22 in PC cell line PANC-1 and BxPC-3.

RESULTS

A total of 153 SMRGs were acquired, of which 48 were linked to PC patients' prognosis. Two SM-related subtypes (SMRGcluster A and B) were identified in PC. SMRGcluster A had a poorer outcome and more active SM process compared to SMRGcluster B. Immune analysis revealed that SMRGcluster B had higher immune and stromal scores and CD8 + T cell abundance, while SMRGcluster A had a higher tumour purity score and M0 macrophages and activated dendritic cell abundance. PC with SMRGcluster B was more susceptible to gemcitabine, paclitaxel, and oxaliplatin. Then SM-related prognostic model (including ANLN, ANKRD22, and DKK1) was built, which had a very good predictive performance. Single-cell analysis revealed that in PC microenvironment, macrophages, epithelial cells, and endothelial cells had relatively higher SM activity. ANKRD22, DKK1, and ANLN have relatively higher expression levels in epithelial cells. Cell subpopulations with high expression of ANKRD22, DKK1, and ANLN had more active SM activity. In vitro experiments showed that ANKRD22 knockdown can inhibit the proliferation, migration, and invasion of PC cells.

CONCLUSION

This study revealed the important significance of SM in PC and identified SM-associated molecular subtypes and prognostic model, which provided novel perspectives on the stratification, prognostic prediction, and precision treatment of PC patients.

Deciphering the role of NcRNAs in Pancreatic Cancer immune evasion and drug resistance: a new perspective for targeted therapy

Pancreatic cancer (PC) is a very aggressive digestive system tumor, known for its high mortality rate, low cure rate, low survival rate and poor prognosis. In particular, pancreatic ductal adenocarcinoma (PADC), which accounts for more than 90% of PC cases, has an overall 5-year survival rate of only 5%, which is an extremely critical situation. Early detection and effective treatment of PC is extremely difficult, which leads many patients to despair. In the current medical context, targeted therapy, as an important strategy for cancer treatment, is expected. However, the problems of immune escape and drug resistance in PC have become two major obstacles that are difficult to be overcome by targeted therapy. How to break through these two difficulties has become a key issue to be solved in the field of PC therapy. In recent years, non-coding RNAs (ncRNAs) have continued to heat up in the field of cancer research. NcRNAs play a pivotal role in gene regulation, cell differentiation, development, and disease processes, and their important roles in the genesis, development, and therapeutic response of PC have been gradually revealed. More importantly, ncRNAs have many advantages as therapeutic targets, such as high specificity and low side effects, making them a new favorite in the field of PC therapy. Therefore, the aim of this paper is to provide new ideas and methods for the targeted therapy of PC by reviewing the mechanism of action of four major ncRNAs (circRNAs, lncRNAs, miRNAs, siRNAs) in both immune escape and drug resistance of PC. It is expected that an effective way to overcome immune escape and drug resistance can be found through in-depth study of ncRNA, bringing a ray of hope to PC patients.

A single-cell perspective on immunotherapy for pancreatic cancer: from microenvironment analysis to therapeutic strategy innovation

Pancreatic cancer remains one of the most lethal malignancies, with conventional treatment options providing limited efficacy. Recent advancements in immunotherapy have offered new hope, yet the unique tumor microenvironment (TME) of pancreatic cancer poses significant challenges to its successful application. This review explores the transformative impact of single-cell technology on the understanding and treatment of pancreatic cancer. By enabling high-resolution analysis of cellular heterogeneity within the TME, single-cell approaches have elucidated the complex interplay between various immune and tumor cell populations. These insights have led to the identification of predictive biomarkers and the development of innovative, personalized immunotherapeutic strategies. The review discusses the role of single-cell technology in dissecting the intricate immune landscape of pancreatic cancer, highlighting the discovery of T cell exhaustion profiles and macrophage polarization states that influence treatment response. Moreover, it outlines the potential of single-cell data in guiding the selection of immunotherapy drugs and optimizing treatment plans. The review also addresses the challenges and prospects of translating these single-cell-based innovations into clinical practice, emphasizing the need for interdisciplinary research and the integration of artificial intelligence to overcome current limitations. Ultimately, the review underscores the promise of single-cell technology in driving therapeutic strategy innovation and improving patient outcomes in the battle against pancreatic cancer.

Targeting the CSF1/CSF1R signaling pathway: an innovative strategy for ultrasound combined with macrophage exhaustion in pancreatic cancer therapy

Pancreatic cancer (PC) is a highly aggressive and lethal malignancy characterized by a complex tumor microenvironment (TME) and immunosuppressive features that limit the efficacy of existing treatments. This paper reviews the potential of combining ultrasound with macrophage exhaustion in the treatment of pancreatic cancer. Macrophages, particularly tumor-associated macrophages (TAMs), are crucial in pancreatic cancer progression and immune escape. Prolonged exposure to the immunosuppressive TME leads to macrophage exhaustion, reducing their anti-tumor ability and instead promoting tumor growth. The CSF1/CSF1R signaling pathway is key in macrophage recruitment and functional regulation, making it an effective target for combating macrophage exhaustion. Ultrasound technology not only plays a significant role in diagnosis and staging but also enhances therapeutic efficacy by guiding radiofrequency ablation (RFA) and percutaneous alcohol injection (PEI) in combination with immunomodulators. Additionally, ultrasound imaging can monitor the number and functional status of TAMs in real-time, providing a basis for optimizing treatment strategies. Future studies should further investigate the combined use of ultrasound and immunomodulators to refine treatment regimens, address challenges such as individual variability and long-term effects, and offer new hope for pancreatic cancer patients.

Exploring copper metabolism-induced cell death in gastric cancer: a single-cell RNA sequencing study and prognostic model development

BACKGROUND

Gastric cancer (GC) is the third leading cause of cancer-related deaths globally. Despite advancements in treatment, the overall 5-year survival rate remains below 30%, particularly in advanced stages. Copper metabolism, vital for various cellular processes, has been linked to cancer progression, but its role in GC, especially at the single-cell level, is not well understood.

OBJECTIVE

This study aims to investigate copper metabolism in GC by integrating single-cell RNA sequencing (scRNA-seq) data and developing a prognostic model based on copper metabolism-related gene (CMRG) expression. The study explores how copper metabolism affects the tumor microenvironment and identifies potential therapeutic targets.

METHODS

scRNA-seq data from gastric cancer and normal tissues were analyzed using the Seurat package. Principal Component Analysis (PCA) and Uniform Manifold Approximation and Projection (UMAP) were used for dimensionality reduction and clustering. Non-negative matrix factorization (NMF) was employed for T cell subpopulation analysis. A high-dimensional weighted gene co-expression network analysis (HdWGCNA) identified key molecular features. LASSO regression and Random Survival Forest (RSF) techniques were used to create and validate a prognostic model. Survival analysis, immune microenvironment assessment, and drug sensitivity analysis were conducted.

RESULTS

Sixteen cell clusters and nine distinct cell types were identified, with T cells showing significant roles in cell communication. The NMF analysis of CD8 +T cells revealed five copper metabolism-related subtypes. The prognostic model based on nine CMRGs indicated significant survival differences between high- and low-risk groups. High-risk patients showed shorter survival times, increased immune cell infiltration, and altered immune responses. Drug sensitivity analysis suggested higher efficacy of certain drugs in high-CMRG patients.

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.

Single-cell RNA sequencing explored potential therapeutic targets by revealing the tumor microenvironment of neuroblastoma and its expression in cell death

BACKGROUND

Neuroblastoma (NB) is the most common extracranial solid tumor in childhood and is closely related to the early development and differentiation of neuroendocrine (NE) cells. The disease is mainly represented by high-risk NB, which has the characteristics of high mortality and difficult treatment. The survival rate of high-risk NB patients is not ideal. In this article, we not only conducted a comprehensive study of NB through single-cell RNA sequencing (scRNA-seq) but also further analyzed cuproptosis, a new cell death pathway, in order to find clinical treatment targets from a new perspective.

MATERIALS AND METHODS

The Seurat software was employed to process the scRNA-seq data. This was followed by the utilization of GO enrichment analysis and GSEA to unveil pertinent enriched pathways. The inferCNV software package was harnessed to investigate chromosomal copy number variations. pseudotime analyses involved the use of Monocle 2, CytoTRACE, and Slingshot software. CellChat was employed to analyze the intercellular communication network for NB. Furthermore, PySCENIC was deployed to review the profile of transcription factors.

RESULT

Using scRNA-seq, we studied cells from patients with NB. NE cells exhibited superior specificity in contrast to other cell types. Among NE cells, C1 PCLAF + NE cells showed a close correlation with the genesis and advancement of NB. The key marker genes, cognate receptor pairing, developmental trajectories, metabolic pathways, transcription factors, and enrichment pathways in C1 PCLAF + NE cells, as well as the expression of cuproptosis in C1 PCLAF + NE cells, provided new ideas for exploring new therapeutic targets for NB.

CONCLUSION

The results revealed the specificity of malignant NE cells in NB, especially the key subset of C1 PCLAF + NE cells, which enhanced our understanding of the key role of the tumor microenvironment in the complexity of cancer progression. Of course, cell death played an important role in the progression of NB, which also promoted our research on new targets. The scrutiny of these findings proved advantageous in uncovering innovative therapeutic targets, thereby bolstering clinical interventions.

Unveiling the cellular landscape: insights from single-cell RNA sequencing in multiple myeloma

Objective

The aim of this research was to gain a thorough understanding of the processes involved in cell communication and discover potential indicators for treating multiple myeloma (MM) through the use of single-cell RNA sequencing (scRNA-seq). And explored the expression of multiple myeloma-related subgroups on metal ion-related pathways to explore the relationship between MM and metal ions.

Methods

We performed a fair examination using single-cell RNA sequencing on 32 bone marrow specimens collected from 22 individuals at different points of MM advancement and 9 individuals without any health issues. To analyze the scRNA-seq data, we employed advanced computational algorithms, including Slingshot, Monocle2, and other methodologies. Specifically, Slingshot and Monocle2 enabled us to simulate the biological functionalities of different cell populations and map trajectories of cell developmental pathways. Additionally, we utilized the UMAP algorithm, a powerful dimension reduction technique, to cluster cells and identify genes that were differentially expressed across clusters.

Results

Our study revealed distinct gene expression patterns and molecular pathways within each patient, which exhibited associations with disease progression. The analysis provided insights into the tumor microenvironment (TME), intra- and inter-patient heterogeneity, and cell-cell interactions mediated by ligand-receptor signaling. And found that multiple myeloma-related subgroups were expressed higher levels in MMP and TIMP pathways, there were some associations.

Conclusion

Our study presents a fresh perspective for future research endeavors and clinical interventions in the field of MM. The identified gene expression patterns and molecular pathways hold immense potential as therapeutic targets for the treatment of multiple myeloma. The utilization of scRNA-seq technology has significantly contributed to a more precise understanding of the complex cellular processes and interactions within MM. Through these advancements, we are now better equipped to unravel the underlying mechanisms driving the development and progression of this complex disease.

Single-cell RNA sequencing reveals that MYBL2 in malignant epithelial cells is involved in the development and progression of ovarian cancer

Background

Ovarian carcinoma (OC) is a prevalent gynecological malignancy associated with high recurrence rates and mortality, often diagnosed at advanced stages. Despite advances in immunotherapy, immune exhaustion remains a significant challenge in achieving optimal tumor control. However, the exploration of intratumoral heterogeneity of malignant epithelial cells and the ovarian cancer tumor microenvironment is still limited, hindering our comprehensive understanding of the disease.

Materials and methods

Utilizing single-cell RNA sequencing (scRNA-seq), we comprehensively investigated the cellular composition across six ovarian cancer patients with omental metastasis. Our focus centered on analysis of the malignant epithelial cells. Employing CytoTRACE and slingshot pseudotime analyses, we identified critical subpopulations and explored associated transcription factors (TFs) influencing ovarian cancer progression. Furthermore, by integrating clinical factors from a large cohort of bulk RNA sequencing data, we have established a novel prognostic model to investigate the impact of the tumor immune microenvironment on ovarian cancer patients. Furthermore, we have investigated the condition of immunological exhaustion.

Results

Our study identified a distinct and highly proliferative subgroup of malignant epithelial cells, known as C2 TOP2A+ TCs. This subgroup primarily consisted of patients who hadn't received neoadjuvant chemotherapy. Ovarian cancer patients with elevated TOP2A expression exhibited heightened sensitivity to neoadjuvant chemotherapy (NACT). Moreover, the transcription factor MYBL2 in this subgroup played a critical role in ovarian cancer development. Additionally, we developed an independent prognostic indicator, the TOP2A TCs Risk Score (TTRS), which revealed a correlation between the High TTRS Group and unfavorable outcomes. Furthermore, immune infiltration and drug sensitivity analyses demonstrated increased responsiveness to Paclitaxel, Cisplatin, and Gemcitabine in the Low TTRS Group.

Conclusion

This research deepens our understanding of malignant epithelial cells in ovarian cancer and enhances our knowledge of the ovarian cancer immune microenvironment and immune exhaustion. We have revealed the heightened susceptibility of the C2 TOP2A+ TCs subgroup to neoadjuvant chemotherapy and emphasized the role of MYBL2 within the C2 subgroup in promoting the occurrence and progression of ovarian cancer. These insights provide valuable guidance for the management of ovarian cancer treatment.

Exploiting cell death and tumor immunity in cancer therapy: challenges and future directions

Cancer remains a significant global challenge, with escalating incidence rates and a substantial burden on healthcare systems worldwide. Herein, we present an in-depth exploration of the intricate interplay between cancer cell death pathways and tumor immunity within the tumor microenvironment (TME). We begin by elucidating the epidemiological landscape of cancer, highlighting its pervasive impact on premature mortality and the pronounced burden in regions such as Asia and Africa. Our analysis centers on the pivotal concept of immunogenic cell death (ICD), whereby cancer cells succumbing to specific stimuli undergo a transformation that elicits robust anti-tumor immune responses. We scrutinize the mechanisms underpinning ICD induction, emphasizing the release of damage-associated molecular patterns (DAMPs) and tumor-associated antigens (TAAs) as key triggers for dendritic cell (DC) activation and subsequent T cell priming. Moreover, we explore the contributions of non-apoptotic RCD pathways, including necroptosis, ferroptosis, and pyroptosis, to tumor immunity within the TME. Emerging evidence suggests that these alternative cell death modalities possess immunogenic properties and can synergize with conventional treatments to bolster anti-tumor immune responses. Furthermore, we discuss the therapeutic implications of targeting the TME for cancer treatment, highlighting strategies to harness immunogenic cell death and manipulate non-apoptotic cell death pathways for therapeutic benefit. By elucidating the intricate crosstalk between cancer cell death and immune modulation within the TME, this review aims to pave the way for the development of novel cancer therapies that exploit the interplay between cell death mechanisms and tumor immunity and overcome Challenges in the Development and implementation of Novel Therapies.

Angiogenesis-related lncRNAs index: A predictor for CESC prognosis, immunotherapy efficacy, and chemosensitivity

Cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC) is a common gynecologic tumor and patients with advanced and recurrent disease usually have a poor clinical outcome. Angiogenesis is involved in the biological processes of tumors and can promote tumor growth and invasion. In this paper, we created a signature for predicting prognosis based on angiogenesis-related lncRNAs (ARLs). This provides a prospective direction for enhancing the efficacy of immunotherapy in CESC patients. We screened seven OS-related ARLs by univariate and multivariate regression analyses and Lasso analysis and developed a prognostic signature at the same time. Then, we performed an internal validation in the TCGA-CESC cohort to increase the precision of the study. In addition, we performed a series of analyses based on ARLs, including immune cell infiltration, immune function, immune checkpoint, tumor mutation load, and drug sensitivity analysis. Our created signature based on ARLs can effectively predict the prognosis of CESC patients. To strengthen the prediction accuracy of the signature, we built a nomogram by combining signature and clinical features.

Single-cell aggrephagy-related patterns facilitate tumor microenvironment intercellular communication, influencing osteosarcoma progression and prognosis

Osteosarcoma, a common malignant tumor in children, has emerged as a major threat to the life and health of pediatric patients. Presently, there are certain limitations in the diagnosis and treatment methods for this disease, resulting in inferior therapeutic outcomes. Therefore, it is of great importance to study its pathogenesis and explore innovative approaches to diagnosis and treatment. In this study, a non-negative matrix decomposition method was employed to conduct a comprehensive investigation and analysis of aggregated autophagy-related genes within 331,394 single-cell samples of osteosarcoma. Through this study, we have elucidated the intricate communication patterns among various cells within the tumor microenvironment. Based on the classification of aggregated autophagy-related genes, we are not only able to more accurately predict patients' prognosis but also offer robust guidance for treatment strategies. The findings of this study hold promise for breakthroughs in the diagnosis and treatment of osteosarcoma, intervention of aggrephagy is expected to improve the survival rate and quality of life of osteosarcoma patients.

Illuminating the immunological landscape: mitochondrial gene defects in pancreatic cancer through a multiomics lens

This comprehensive review delves into the complex interplay between mitochondrial gene defects and pancreatic cancer pathogenesis through a multiomics approach. By amalgamating data from genomic, transcriptomic, proteomic, and metabolomic studies, we dissected the mechanisms by which mitochondrial genetic variations dictate cancer progression. Emphasis has been placed on the roles of these genes in altering cellular metabolic processes, signal transduction pathways, and immune system interactions. We further explored how these findings could refine therapeutic interventions, with a particular focus on precision medicine applications. This analysis not only fills pivotal knowledge gaps about mitochondrial anomalies in pancreatic cancer but also paves the way for future investigations into personalized therapy options. This finding underscores the crucial nexus between mitochondrial genetics and oncological immunology, opening new avenues for targeted cancer treatment strategies.

Autophagy-related gene model as a novel risk factor for schizophrenia

Autophagy, a cellular process where cells degrade and recycle their own components, has garnered attention for its potential role in psychiatric disorders, including schizophrenia (SCZ). This study aimed to construct and validate a new autophagy-related gene (ARG) risk model for SCZ. First, we analyzed differential expressions in the GSE38484 training set, identifying 4,754 differentially expressed genes (DEGs) between SCZ and control groups. Using the Human Autophagy Database (HADb) database, we cataloged 232 ARGs and pinpointed 80 autophagy-related DEGs (AR-DEGs) after intersecting them with DEGs. Subsequent analyses, including metascape gene annotation, pathway and process enrichment, and protein-protein interaction enrichment, were performed on the 80 AR-DEGs to delve deeper into their biological roles and associated molecular pathways. From this, we identified 34 candidate risk AR-DEGs (RAR-DEGs) and honed this list to final RAR-DEGs via a constructed and optimized logistic regression model. These genes include VAMP7, PTEN, WIPI2, PARP1, DNAJB9, SH3GLB1, ATF4, EIF4G1, EGFR, CDKN1A, CFLAR, FAS, BCL2L1 and BNIP3. Using these findings, we crafted a nomogram to predict SCZ risk for individual samples. In summary, our study offers deeper insights into SCZ's molecular pathogenesis and paves the way for innovative approaches in risk prediction, gene-targeted diagnosis, and community-based SCZ treatments.

Long non-coding RNA signature for predicting gastric cancer survival based on genomic instability

BACKGROUND

Gastric cancer is a prevalent type of tumor with a poor prognosis. Given the high occurrence of genomic instability in gastric cancer, it is essential to investigate the prognostic significance of genes associated with genomic instability in this disease.

METHODS

We identified genomic instability-related lncRNAs (GInLncRNAs) by analyzing somatic mutation and transcriptome profiles. We evaluated co-expression and enrichment using various analyses, including univariate COX analysis and LASSO regression. Based on these findings, we established an lncRNA signature associated with genomic instability, which we subsequently assessed for prognostic value, immune cell and checkpoint analysis, drug sensitivity, and external validation. Finally, PCR assay was used to verify the expression of key lncRNAs.

RESULTS

Our study resulted in the establishment of a seven-lncRNA prognostic signature, including PTENP1-AS, LINC00163, RP11-169F17.1, C8ORF87, RP11-389G6.3, LINCO1210, and RP11-115H13.1. This signature exhibited independent prognostic value and was associated with specific immune cells and checkpoints in gastric cancer. Additionally, the model was correlated with somatic mutation and several chemotherapeutic drugs. We further confirmed the prognostic value of LINC00163, which was included in our model, in an independent dataset. Our model demonstrated superior performance compared to other models. PCR showed that LINC00163 was significantly up-regulated in 4 adjacent normal tissues compared with the GC tissues.

CONCLUSIONS

Our study resulted in the establishment of a seven-lncRNA signature associated with genomic instability, which demonstrated robust prognostic value in predicting the prognosis of gastric cancer. The signature also identified potential chemotherapeutic drugs, making it a valuable tool for clinical decision-making and medication use.

Machine learning-enhanced insights into sphingolipid-based prognostication: revealing the immunological landscape and predictive proficiency for immunomotherapy and chemotherapy responses in pancreatic carcinoma

Background: With a poor prognosis for affected individuals, pancreatic adenocarcinoma (PAAD) is known as a complicated and diverse illness. Immunocytes have become essential elements in the development of PAAD. Notably, sphingolipid metabolism has a dual function in the development of tumors and the invasion of the immune system. Despite these implications, research on the predictive ability of sphingolipid variables for PAAD prognosis is strikingly lacking, and it is yet unclear how they can affect PAAD immunotherapy and targeted pharmacotherapy. Methods: The investigation process included SPG detection while also being pertinent to the prognosis for PAAD. Both the analytical capability of CIBERSORT and the prognostic capability of the pRRophetic R package were used to evaluate the immunological environments of the various HCC subtypes. In addition, CCK-8 experiments on PAAD cell lines were carried out to confirm the accuracy of drug sensitivity estimates. The results of these trials, which also evaluated cell survival and migratory patterns, confirmed the usefulness of sphingolipid-associated genes (SPGs). Results: As a result of this thorough investigation, 32 SPGs were identified, each of which had a measurable influence on the dynamics of overall survival. This collection of genes served as the conceptual framework for the development of a prognostic model, which was carefully assembled from 10 chosen genes. It should be noted that this grouping of patients into cohorts with high and low risk was a sign of different immune profiles and therapy responses. The increased abundance of SPGs was identified as a possible sign of inadequate responses to immune-based treatment approaches. The careful CCK-8 testing carried out on PAAD cell lines was of the highest importance for providing clear confirmation of drug sensitivity estimates. Conclusion: The significance of Sphingolipid metabolism in the complex web of PAAD development is brought home by this study. The novel risk model, built on the complexity of sphingolipid-associated genes, advances our understanding of PAAD and offers doctors a powerful tool for developing personalised treatment plans that are specifically suited to the unique characteristics of each patient.

Unraveling the role of disulfidptosis-related LncRNAs in colon cancer: a prognostic indicator for immunotherapy response, chemotherapy sensitivity, and insights into cell death mechanisms

Background: Colon cancer, a prevalent and deadly malignancy worldwide, ranks as the third leading cause of cancer-related mortality. Disulfidptosis stress triggers a unique form of programmed cell death known as disulfidoptosis, characterized by excessive intracellular cystine accumulation. This study aimed to establish reliable bioindicators based on long non-coding RNAs (LncRNAs) associated with disulfidptosis-induced cell death, providing novel insights into immunotherapeutic response and prognostic assessment in patients with colon adenocarcinoma (COAD). Methods: Univariate Cox proportional hazard analysis and Lasso regression analysis were performed to identify differentially expressed genes strongly associated with prognosis. Subsequently, a multifactorial model for prognostic risk assessment was developed using multiple Cox proportional hazard regression. Furthermore, we conducted comprehensive evaluations of the characteristics of disulfidptosis response-related LncRNAs, considering clinicopathological features, tumor microenvironment, and chemotherapy sensitivity. The expression levels of prognosis-related genes in COAD patients were validated using quantitative real-time fluorescence PCR (qRT-PCR). Additionally, the role of ZEB1-SA1 in colon cancer was investigated through CCK8 assays, wound healing experiment and transwell experiments. Results: disulfidptosis response-related LncRNAs were identified as robust predictors of COAD prognosis. Multifactorial analysis revealed that the risk score derived from these LncRNAs served as an independent prognostic factor for COAD. Patients in the low-risk group exhibited superior overall survival (OS) compared to those in the high-risk group. Accordingly, our developed Nomogram prediction model, integrating clinical characteristics and risk scores, demonstrated excellent prognostic efficacy. In vitro experiments demonstrated that ZEB1-SA1 promoted the proliferation and migration of COAD cells. Conclusion: Leveraging medical big data and artificial intelligence, we constructed a prediction model for disulfidptosis response-related LncRNAs based on the TCGA-COAD cohort, enabling accurate prognostic prediction in colon cancer patients. The implementation of this model in clinical practice can facilitate precise classification of COAD patients, identification of specific subgroups more likely to respond favorably to immunotherapy and chemotherapy, and inform the development of personalized treatment strategies for COAD patients based on scientific evidence.

Revealing the diagnostic value and immune infiltration of senescence-related genes in endometriosis: a combined single-cell and machine learning analysis

Introduction: Endometriosis is a prevalent and recurrent medical condition associated with symptoms such as pelvic discomfort, dysmenorrhea, and reproductive challenges. Furthermore, it has the potential to progress into a malignant state, significantly impacting the quality of life for affected individuals. Despite its significance, there is currently a lack of precise and non-invasive diagnostic techniques for this condition. Methods: In this study, we leveraged microarray datasets and employed a multifaceted approach. We conducted differential gene analysis, implemented weighted gene co-expression network analysis (WGCNA), and utilized machine learning algorithms, including random forest, support vector machine, and LASSO analysis, to comprehensively explore senescence-related genes (SRGs) associated with endometriosis. Discussion: Our comprehensive analysis, which also encompassed profiling of immune cell infiltration and single-cell analysis, highlights the therapeutic potential of this gene assemblage as promising targets for alleviating endometriosis. Furthermore, the integration of these biomarkers into diagnostic protocols promises to enhance diagnostic precision, offering a more effective diagnostic journey for future endometriosis patients in clinical settings. Results: Our meticulous investigation led to the identification of a cluster of genes, namely BAK1, LMNA, and FLT1, which emerged as potential discerning biomarkers for endometriosis. These biomarkers were subsequently utilized to construct an artificial neural network classifier model and were graphically represented in the form of a Nomogram.

Decoding tumor heterogeneity in uveal melanoma: basement membrane genes as novel biomarkers and therapeutic targets revealed by multi-omics approaches for cancer immunotherapy

Background: Uveal melanoma (UVM) is a primary intraocular malignancy that poses a significant threat to patients' visual function and life. The basement membrane (BM) is critical for establishing and maintaining cell polarity, adult function, embryonic and organ morphogenesis, and many other biological processes. Some basement membrane protein genes have been proven to be prognostic biomarkers for various cancers. This research aimed to develop a novel risk assessment system based on BMRGs that would serve as a theoretical foundation for tailored and accurate treatment. Methods: We used gene expression profiles and clinical data from the TCGA-UVM cohort of 80 UVM patients as a training set. 56 UVM patients from the combined cohort of GSE84976 and GSE22138 were employed as an external validation dataset. Prognostic characteristics of basement membrane protein-related genes (BMRGs) were characterized by Lasso, stepwise multifactorial Cox. Multivariate analysis revealed BMRGs to be independent predictors of UVM. The TISCH database probes the crosstalk of BMEGs in the tumor microenvironment at the single-cell level. Finally, we investigated the function of ITGA5 in UVM using multiple experimental techniques, including CCK8, transwell, wound healing assay, and colony formation assay. Results: There are three genes in the prognostic risk model (ADAMTS10, ADAMTS14, and ITGA5). After validation, we determined that the model is quite reliable and accurately forecasts the prognosis of UVM patients. Immunotherapy is more likely to be beneficial for UVM patients in the high-risk group, whereas the survival advantage may be greater for UVM patients in the low-risk group. Knockdown of ITGA5 expression was shown to inhibit the proliferation, migration, and invasive ability of UVM cells in vitro experiments. Conclusion: The 3-BMRGs feature model we constructed has excellent predictive performance which plays a key role in the prognosis, informing the individualized treatment of UVM patients. It also provides a new perspective for assessing pre-immune efficacy.

Uncovering the immune microenvironment and molecular subtypes of hepatitis B-related liver cirrhosis and developing stable a diagnostic differential model by machine learning and artificial neural networks

Background: Hepatitis B-related liver cirrhosis (HBV-LC) is a common clinical disease that evolves from chronic hepatitis B (CHB). The development of cirrhosis can be suppressed by pharmacological treatment. When CHB progresses to HBV-LC, the patient's quality of life decreases dramatically and drug therapy is ineffective. Liver transplantation is the most effective treatment, but the lack of donor required for transplantation, the high cost of the procedure and post-transplant rejection make this method unsuitable for most patients. Methods: The aim of this study was to find potential diagnostic biomarkers associated with HBV-LC by bioinformatics analysis and to classify HBV-LC into specific subtypes by consensus clustering. This will provide a new perspective for early diagnosis, clinical treatment and prevention of HCC in HBV-LC patients. Two study-relevant datasets, GSE114783 and GSE84044, were retrieved from the GEO database. We screened HBV-LC for feature genes using differential analysis, weighted gene co-expression network analysis (WGCNA), and three machine learning algorithms including least absolute shrinkage and selection operator (LASSO), support vector machine recursive feature elimination (SVM-RFE), and random forest (RF) for a total of five methods. After that, we constructed an artificial neural network (ANN) model. A cohort consisting of GSE123932, GSE121248 and GSE119322 was used for external validation. To better predict the risk of HBV-LC development, we also built a nomogram model. And multiple enrichment analyses of genes and samples were performed to understand the biological processes in which they were significantly enriched. And the different subtypes of HBV-LC were analyzed using the Immune infiltration approach. Results: Using the data downloaded from GEO, we developed an ANN model and nomogram based on six feature genes. And consensus clustering of HBV-LC classified them into two subtypes, C1 and C2, and it was hypothesized that patients with subtype C2 might have milder clinical symptoms by immune infiltration analysis. Conclusion: The ANN model and column line graphs constructed with six feature genes showed excellent predictive power, providing a new perspective for early diagnosis and possible treatment of HBV-LC. The delineation of HBV-LC subtypes will facilitate the development of future clinical treatment of HBV-LC.

Multi-omics analyses based on genes associated with oxidative stress and phospholipid metabolism revealed the intrinsic molecular characteristics of pancreatic cancer

Oxidative stress (OS), which impacts lipid metabolic reprogramming, can affect the biological activities of cancer cells. How oxidative stress and phospholipid metabolism (OSPM) influence the prognosis of pancreatic cancer (PC) needs to be elucidated. The metabolic data of 35 pancreatic tumor samples, 34 para-carcinoma samples, and 31 normal pancreatic tissues were obtained from the previously published literature. Pan-cancer samples were obtained from The Cancer Genome Atlas (TCGA). And the Gene Expression Omnibus (GEO), International Cancer Genome Consortium (ICGC), ArrayExpress, and the Genotype-Tissue Expression (GTEx) databases were searched for more PC and normal pancreatic samples. The metabolites in PC were compared with normal and para-carcinoma tissues. The characteristics of the key OSPM genes were summarized in pan-cancer. The random survival forest analysis and multivariate Cox regression analysis were utilized to construct an OSPM-related signature. Based on this signature, PC samples were divided into high- and low-risk subgroups. The dysregulations of the tumor immune microenvironment were further investigated. Quantitative reverse transcription polymerase chain reaction (qRT-PCR) was conducted to investigate the expression of genes in the signature in PC and normal tissues. The protein levels of these genes were further demonstrated. In PC, metabolomic studies revealed the alteration of PM, while transcriptomic studies showed different expressions of OSPM-related genes. Then 930 PC samples were divided into three subtypes with different prognoses, and an OSPM-related signature including eight OSPM-related genes (i.e., SLC2A1, MMP14, TOP2A, MBOAT2, ANLN, ECT2, SLC22A3, and FGD6) was developed. High- and low-risk subgroups divided by the signature showed different prognoses, expression levels of immune checkpoint genes, immune cell infiltration, and tumor microenvironment. The risk score was negatively correlated with the proportion of TIL, pDC, Mast cell, and T cell co-stimulation. The expression levels of genes in the signature were verified in PC and normal samples. The protein levels of SLC2A1, MMP14, TOP2A, MBOAT2, ANLN, and SLC22A3 showed up-regulation in PC samples compared with normal tissues. After integrating metabolomics and transcriptomics data, the alterations in OSPM in PC were investigated, and an OSPM-related signature was developed, which was helpful for the prognostic assessment and individualized treatment for PC.

GADD45B regulates the carcinogenesis process of chronic atrophic gastritis and the metabolic pathways of gastric cancer

Background

Gastric cancer continues to be a significant global healthcare challenge, and its burden remains substantial. The development of gastric cancer (GC) is closely linked to chronic atrophic gastritis (CAG), yet there is a scarcity of research exploring the underlying mechanisms of CAG-induced carcinogenesis.

Methods

In this study, we conducted a comprehensive investigation into the oncogenes involved in CAG using both bulk transcriptome and single-cell transcriptome data. Our approach employed hdWGCNA to identify pathogenic genes specific to CAG, with non-atrophic gastritis (NAG) serving as the control group. Additionally, we compared CAG with GC, using normal gastric tissue as the control group in the single-cell transcriptome analysis. By intersecting the identified pathogenic genes, we pinpointed key network molecules through protein interaction network analysis. To further refine the gene selection, we applied LASSO, SVM-RFE, and RF techniques, which resulted in a set of cancer-related genes (CRGs) associated with CAG. To identify CRGs potentially linked to gastric cancer progression, we performed a univariate COX regression analysis on the gene set. Subsequently, we explored the relationship between CRGs and immune infiltration, drug sensitivity, and clinical characteristics in gastric cancer patients. We employed GSVA to investigate how CRGs regulated signaling pathways in gastric cancer cells, while an analysis of cell communication shed light on the impact of CRGs on signal transmission within the gastric cancer tumor microenvironment. Lastly, we analyzed changes in metabolic pathways throughout the progression of gastric cancer.

Results

Using hdWGCNA, we have identified a total of 143 pathogenic genes that were shared by CAG and GC. To further investigate the underlying mechanisms, we conducted protein interaction network analysis and employed machine learning screening techniques. As a result, we have identified 15 oncogenes that are specifically associated with chronic atrophic gastritis. By performing ROC reanalysis and prognostic analysis, we have determined that GADD45B is the most significant gene involved in the carcinogenesis of CAG. Immunohistochemical staining and differential analysis have revealed that GADD45B expression was low in GC tissues while high in normal gastric tissues. Moreover, based on prognostic analysis, high expression of GADD45B has been correlated with poor prognosis in GC patients. Additionally, an analysis of immune infiltration has shown a relationship between GADD45B and the infiltration of various immune cells. By correlating GADD45B with clinical characteristics, we have found that it primarily affects the depth of invasion in GC. Through cell communication analysis, we have discovered that the CD99 signaling pathway network and the CDH signaling pathway network are the main communication pathways that significantly alter the microenvironment of gastric tissue during the development of chronic atrophic gastritis. Specifically, GADD45B-low GC cells were predominantly involved in the network communication of the CDH signaling pathway, while GADD45B-high GC cells played a crucial role in both signaling pathways. Furthermore, we have identified several metabolic pathways, including D-Glutamine and D-glutamate metabolism and N-Glycan biosynthesis, among others, that played important roles in the occurrence and progression of GC, in addition to the six other metabolic pathways. In summary, our study highlighted the discovery of 143 pathogenic genes shared by CAG and GC, with a specific focus on 15 oncogenes associated with CAG. We have identified GADD45B as the most important gene in the carcinogenesis of CAG, which exhibited differential expression in GC tissues compared to normal gastric tissues. Moreover, GADD45B expression was correlated with patient prognosis and is associated with immune cell infiltration. Our findings also emphasized the impact of the CD99 and CDH signaling pathway networks on the microenvironment of gastric tissue during the development of CAG. Additionally, we have identified key metabolic pathways involved in GC progression.

Conclusion

GADD45B, an oncogene implicated in chronic atrophic gastritis, played a critical role in GC development. Decreased expression of GADD45B was associated with the onset of GC. Moreover, GADD45B expression levels were closely tied to poor prognosis in GC patients, influencing the infiltration patterns of various cells within the tumor microenvironment, as well as impacting the metabolic pathways involved in GC progression.

Proposing new early detection indicators for pancreatic cancer: Combining machine learning and neural networks for serum miRNA-based diagnostic model

BACKGROUND

Pancreatic cancer (PC) is a lethal malignancy that ranks seventh in terms of global cancer-related mortality. Despite advancements in treatment, the five-year survival rate remains low, emphasizing the urgent need for reliable early detection methods. MicroRNAs (miRNAs), a group of non-coding RNAs involved in critical gene regulatory mechanisms, have garnered significant attention as potential diagnostic and prognostic biomarkers for pancreatic cancer (PC). Their suitability stems from their accessibility and stability in blood, making them particularly appealing for clinical applications.

METHODS

In this study, we analyzed serum miRNA expression profiles from three independent PC datasets obtained from the Gene Expression Omnibus (GEO) database. To identify serum miRNAs associated with PC incidence, we employed three machine learning algorithms: Support Vector Machine-Recursive Feature Elimination (SVM-RFE), Least Absolute Shrinkage and Selection Operator (LASSO), and Random Forest. We developed an artificial neural network model to assess the accuracy of the identified PC-related serum miRNAs (PCRSMs) and create a nomogram. These findings were further validated through qPCR experiments. Additionally, patient samples with PC were classified using the consensus clustering method.

RESULTS

Our analysis revealed three PCRSMs, namely hsa-miR-4648, hsa-miR-125b-1-3p, and hsa-miR-3201, using the three machine learning algorithms. The artificial neural network model demonstrated high accuracy in distinguishing between normal and pancreatic cancer samples, with verification and training groups exhibiting AUC values of 0.935 and 0.926, respectively. We also utilized the consensus clustering method to classify PC samples into two optimal subtypes. Furthermore, our investigation into the expression of PCRSMs unveiled a significant negative correlation between the expression of hsa-miR-125b-1-3p and age.

CONCLUSION

Our study introduces a novel artificial neural network model for early diagnosis of pancreatic cancer, carrying significant clinical implications. Furthermore, our findings provide valuable insights into the pathogenesis of pancreatic cancer and offer potential avenues for drug screening, personalized treatment, and immunotherapy against this lethal disease.

Long Non-Coding RNAs and Metabolic Rewiring in Pancreatic Cancer

Pancreatic adenocarcinoma is a highly aggressive disease with a poor prognosis. The reprogramming of energetic metabolism has long been implicated in pancreatic tumorigenesis and/or resistance to treatment. Considering that long non-coding RNA dysregulation has been described both in cancerogenesis and in the altered homeostasis of several metabolic pathways, metabolism-associated lncRNAs can contribute to pancreatic cancer evolution. The objective of this review is to assess the burden of lncRNA dysregulation in pancreatic cancer metabolic reprogramming, and its effect on this tumor's natural course and response to treatment. Therefore, we reviewed the available literature to assess whether metabolism-associated lncRNAs have been found to be differentially expressed in pancreatic cancer, as well as whether experimental evidence of their role in such pathways can be demonstrated. Specifically, we provide a comprehensive overview of lncRNAs that are implicated in hypoxia-related pathways, as well as in the reprogramming of autophagy, lipid metabolism, and amino acid metabolism. Our review gathers background material for further research on possible applications of metabolism-associated lncRNAs as diagnostic/prognostic biomarkers and/or as potential therapeutic targets in pancreatic adenocarcinoma.

FAM family gene prediction model reveals heterogeneity, stemness and immune microenvironment of UCEC

Background: Endometrial cancer (UCEC) is a highly heterogeneous gynecologic malignancy that exhibits variable prognostic outcomes and responses to immunotherapy. The Familial sequence similarity (FAM) gene family is known to contribute to the pathogenesis of various malignancies, but the extent of their involvement in UCEC has not been systematically studied. This investigation aimed to develop a robust risk profile based on FAM family genes (FFGs) to predict the prognosis and suitability for immunotherapy in UCEC patients. Methods: Using the TCGA-UCEC cohort from The Cancer Genome Atlas (TCGA) database, we obtained expression profiles of FFGs from 552 UCEC and 35 normal samples, and analyzed the expression patterns and prognostic relevance of 363 FAM family genes. The UCEC samples were randomly divided into training and test sets (1:1), and univariate Cox regression analysis and Lasso Cox regression analysis were conducted to identify the differentially expressed genes (FAM13C, FAM110B, and FAM72A) that were significantly associated with prognosis. A prognostic risk scoring system was constructed based on these three gene characteristics using multivariate Cox proportional risk regression. The clinical potential and immune status of FFGs were analyzed using CiberSort, SSGSEA, and tumor immune dysfunction and rejection (TIDE) algorithms. qRT-PCR and IHC for detecting the expression levels of 3-FFGs. Results: Three FFGs, namely, FAM13C, FAM110B, and FAM72A, were identified as strongly associated with the prognosis of UCEC and effective predictors of UCEC prognosis. Multivariate analysis demonstrated that the developed model was an independent predictor of UCEC, and that patients in the low-risk group had better overall survival than those in the high-risk group. The nomogram constructed from clinical characteristics and risk scores exhibited good prognostic power. Patients in the low-risk group exhibited a higher tumor mutational load (TMB) and were more likely to benefit from immunotherapy. Conclusion: This study successfully developed and validated novel biomarkers based on FFGs for predicting the prognosis and immune status of UCEC patients. The identified FFGs can accurately assess the prognosis of UCEC patients and facilitate the identification of specific subgroups of patients who may benefit from personalized treatment with immunotherapy and chemotherapy.