Mechanism investigation and experiment validation of capsaicin on uterine corpus endometrial carcinoma

Single-cell atlas of endothelial cells in atherosclerosis: identifying C1 CXCL12+ ECs as key proliferative drivers for immunological precision therapeutics in atherosclerosis

Background

Atherosclerosis (AS) is a chronic inflammatory disease characterized by endothelial dysfunction, monocyte infiltration, smooth muscle proliferation, and extracellular matrix accumulation. Endothelial cell (EC) dysfunction plays a pivotal role in the initiation and progression of AS. Despite progress in traditional research methods, the complexity of cellular heterogeneity within the disease remains poorly understood, necessitating a more refined approach for uncovering disease mechanisms.

Methods

In this study, we employed single-cell RNA sequencing (scRNA-seq) to map the endothelial cell landscape in AS comprehensively. By analyzing cellular heterogeneity, differentiation trajectories, and functional states, we identified critical endothelial subpopulations and their roles in the progression of AS. Functional enrichment and differentiation analyses were conducted, and the findings were validated through in vitro experiments.

Results

The single-cell analysis revealed distinct EC subpopulations with unique contributions to AS progression. Among these, C1 CXCL12+ ECs emerged as a key subpopulation associated with endothelial differentiation, vascular remodeling, and inflammation. These cells demonstrated high proliferative potential and were enriched in pathways related to endothelial migration and repair. Through CCK-8, Transwell assay, EdU staining and angiogenesis ability, we found that knockdown of FOXM1 in C1 CXCL12+ ECs resulted in decreased proliferation, migration and invasion. Thus, it affects the progression of AS.

Conclusion

This study provides a detailed single-cell atlas of endothelial cells in AS, identifying critical subpopulations, regulatory pathways, and key factors driving disease progression. The application of single-cell technologies paves the way for advancing our understanding of cardiovascular diseases and offers significant potential for developing personalized therapeutic strategies in immunology and precision medicine.

Decoding multiple myeloma: single-cell insights into tumor heterogeneity, immune dynamics, and disease progression

Background

Multiple myeloma (MM) is a biologically heterogeneous malignancy of clonal plasma cells, often progressing from MGUS or smoldering MM. It causes anemia, bone lesions, and immune dysfunction due to abnormal plasma cell expansion in the bone marrow. Neuroinflammatory and neurotrophic factors may influence MM progression by affecting immune cells and the bone marrow niche. Growing evidence points to a role for neuroimmune regulation in tumor immunity. Despite therapeutic progress, disease heterogeneity and resistance highlight the need for new strategies targeting the tumor microenvironment and neuroimmune axis.

Methods

This investigation exploited single-cell RNA sequencing (scRNA-seq) to analyze MM and high-risk smoldering multiple myeloma (SMMh) samples, identifying 11 distinct cell types. We examined their transcriptional signatures, stemness, proliferative properties, and metabolic pathways, with particular attention to neuroimmune interactions in the tumor microenvironment. Using trajectory inference tools such as CytoTRACE, Monocle2, and Slingshot, we traced the differentiation paths of MM cell subpopulations and identified key signaling pathways that may influence immune responses and tumor progression.

Results

The analysis identified four distinct subpopulations of myeloma cells, with the C0 IGLC3+ myeloma cells representing the least differentiated and most proliferative subset. These cells played a critical role in MM progression and may contribute to immune evasion mechanisms. Additionally, receptor-ligand interactions within the tumor microenvironment were identified, which may be influenced by neuroinflammatory and neurotrophic factors. These findings suggest that the nervous system and immune modulation significantly affect tumor biology, highlighting potential therapeutic targets that could be exploited to overcome resistance to conventional therapies.

Conclusion

This single-cell analysis provided new insights into the cellular diversity and differentiation trajectories in MM, offering a deeper understanding of the complex neuroimmune interactions that drive tumor progression and resistance. By incorporating the role of neuroinflammation and immune modulation, our study suggested novel therapeutic strategies targeting the neuroimmune axis in oncology, ultimately contributing to the development of more effective, personalized treatment approaches for MM.

Pancancer Analysis of NSUN2 with a Focus on Prognostic and Immunological Roles in Endometrial Cancer

The significance of NSUN2 in carcinogenesis is gradually being recognized, yet a comprehensive analysis across pan-cancer remains a pivotal void in existing research. In our investigation, we capitalized on the UCSC Xena platform to evaluate NSUN2 expression levels and their prognostic implications across a range of cancer types. Furthermore, we employed the cBioPortal database to delve into the genomic variations of NSUN2 within human cancers. Our study encompassed the use of molecular docking, genomic tumor profiling, and an assessment of the gene's responsiveness to pharmacological treatments. Additionally, we utilized algorithmic techniques to measure the relationship between NSUN2 expression and key clinical biomarkers, such as microsatellite instability (MSI), tumor mutational burden (TMB), and immune cell infiltration. Our results have established a notable association between NSUN2 and endometrial cancer (UCEC), thereby confirming its clinical significance through an analysis of tumoral expression patterns, mutational spectra, methylation profiles, and drug sensitivity. Gene Set Enrichment Analysis (GSEA) and Gene Set Variation Analysis (GSVA) were crucial tools in elucidating the biological roles of NSUN2 in endometrial cancer. Consistently, elevated NSUN2 expression was associated with unfavorable clinical outcomes and was primarily observed in the context of genetic amplifications. Across 22 distinct tumor types, our analysis revealed a notable correlation between NSUN2 expression and various metrics related to immune cell infiltration, tumor stroma, and immune scores. Notably, higher levels of NSUN2 expression have been linked to a reduced response to certain chemotherapeutic agents, including PHA-793887. In UCEC, a positive correlation between NSUN2 methylation and gene expression hints at a potential epigenetic regulatory mechanism underlying cancer progression. Our study highlights the potential of NSUN2 as a key oncogene and its promising role as a therapeutic target as well as a prognostic biomarker for endometrial cancer. This underscores its potential importance in predicting responses to immunotherapy.

The Potential Role of C4 MYH11+ Fibroblasts and the MDK-SDC2 Ligand-Receptor Pair in Lung Adenocarcinoma: Implications for Prognosis and Therapeutic Strategies

BACKGROUND

Lung adenocarcinoma (LUAD) posed a significant threat to global human health. This study employed single-cell RNA sequencing (scRNA-seq) to analyze transcriptomic data from nine LUAD patients at different stages of tumor infiltration, aiming to elucidate the tumor microenvironment and key biological processes of LUAD.

METHODS

In this study, we processed the scRNA-seq data using the Seurat package and sequentially applied principal component analysis followed by the Harmony package to effectively correct for batch effects, identifying 105,725 high-quality cells. Through cell clustering and gene expression profiling, we identified critical cell subpopulations and gene expression patterns in LUAD patients.

RESULTS

Our analysis revealed that the C4 MYH11+ Fibroblasts subtype was primarily involved in biological processes related to muscle function. Further investigations uncovered the MDK-SDC2 ligand-receptor pair as a critical regulator of tumor cell invasion, proliferation, and migration, driving LUAD progression. Additionally, we developed a gene-based prognostic model that effectively predicted patient survival, providing valuable clinical insights.

CONCLUSION

This study provided a comprehensive atlas of the LUAD tumor microenvironment, highlighted the role of the C4 MYH11+ Fibroblasts in tumor progression. It also proposed the MDK-SDC2 ligand-receptor pair as a novel mechanism, addressing a significant gap in this area of research. And presented a gene-based prognostic model as a novel perspective for research into immunotherapy and drug sensitivity in LUAD.

Single-cell insights into HNSCC tumor heterogeneity and programmed cell death pathways

BACKGROUND

Head and neck squamous cell carcinoma (HNSCC) is a prevalent malignancy often diagnosed in advanced stages. Despite advancements in therapy, it retains a high mortality rate and significant recurrence risk. This study utilizes single-cell sequencing (scRNA-seq) to unravel HNSCC's complexity, identify therapeutic targets, and refine prognostic models.

METHODS

Pseudotime trajectory and stemness analyses were performed on HNSCC tumor subpopulations, focusing on the C2 MALAT1+ Tumors subpopulation, which had the lowest CytoTRACE Score and represented the Lineage 2 endpoint in Slingshot analysis. The study examined programmed death and metabolic pathways in each subpopulation and developed a novel prognostic model using LASSO regression.

RESULTS

The C2 MALAT1+ Tumors subpopulation exhibited reduced expression of programmed death pathways (e.g., Entotic cell death, Apoptosis, Pyroptosis) and metabolic pathways (e.g., Riboflavin metabolism, Glycolysis/Gluconeogenesis). Key transcription factors included LEF1, RFX3, CREM, MZF1, and ZNF202. Prognostic models based on the MALAT1 Tumors Risk Score (MTRS) revealed worse survival and higher tumor purity in the high MTRS group. Risk genes included ADM, RPL31, EIF5B, and TAF7. Additionally, activated CD4 memory T cells were enriched in the high MTRS group, which also showed greater sensitivity to Cisplatin, Docetaxel, and Paclitaxel.

CONCLUSIONS

ScRNA-seq revealed the heterogeneity of HNSCC subpopulations, highlighting the unique features of the C2 MALAT1+ Tumors subpopulation. This study identified novel prognostic markers and therapeutic targets, offering insights into HNSCC progression, drug resistance, and potential treatments.

Discovering the Potential Role of the C2 DUSP2+ MCs Subgroup in Lung Adenocarcinoma

OBJECTIVE

In both industrialized and developing nations worldwide, lung adenocarcinoma is one of the deadliest malignant tumors and the primary cause of cancer-related deaths. Its cellular heterogeneity is unclear to the fullest extent, although in recent years, its prevalence in younger individuals has increased. Therefore, it is urgent to deepen the understanding of lung adenocarcinoma and explore new therapeutic methods.

METHODS

CytoTRACE, Monocle, SCENIC, and enrichment analysis were used to analyze the single cell RNA data, we characterized the biological characteristics of mast cells (MCs) in lung adenocarcinoma patient samples. CellChat was used to analyze and validate the interaction between MCs and tumor cells in lung adenocarcinoma. Prognostic models were used to evaluate and predict the development trend and outcome of a patient's disease, such as the survival time of cancer patients. The python package SCENIC was used to evaluate the enrichment of transcription factors and the activity of regulators in lung adenocarcinoma cell subgroups. CCK-8 assay could validate the activity of a specific cell subgroup sequenced in single cell sequencing to confirm the role of this cell subgroup in tumor proliferation.

RESULTS

Our analysis identified seven major cell types, further grouping MCs within them and identifying four distinct subgroups, including MCs with high DUSP2 expression, which showed some tumor-related characteristics. In addition, we identified the key signaling receptor EGFR and validated it through in vitro knockdown experiments, demonstrating its role in promoting cancer. In addition, we established an independent prognostic indicator, the DUSP2+ MCs risk score, which showed an association between groups with high risk scores and poor outcomes.

CONCLUSION

These findings shed light on the complex interactions in the lung adenocarcinoma tumor microenvironment and suggest that targeting specific MCs subgroups, particularly through the EGFR signaling pathway, may provide new therapeutic strategies.

Exposing the cellular situation: findings from single cell RNA sequencing in breast cancer

Background

Breast Cancer (BC) ranks among the top three most prevalent cancers globally and stands as the principal contributor to cancer-related fatalities among women. In spite of the substantial occurrence rate of BC, the early stage of this disease is generally regarded as curable. However, intra-tumor heterogeneity presents a formidable obstacle to the success of effective treatment.

Method

In this research, single cell RNA sequencing was utilized to dissect the tumor microenvironment within BC. Slingshot, CytoTRACE and Monocle 2 were applied to illustrate the differentiation process of each subpopulation in the pseudotime sequence. To comprehensively comprehend the tumor cells (TCs) in BC, an analysis of upstream transcription factors was carried out via pySCENIC, while downstream pathway enrichment was conducted through KEGG, GO and GSEA. The prognosis model was established based on the bulk data obtained from TCGA and GEO databases. Knock-down experiments were also implemented to explore the function of the transcription factor CEBPD in the TCs.

Results

Our in-depth analysis identified eight principal cell types. Notably, TCs were predominantly found within epithelial cells. The classification of TCs further uncovered five unique subpopulations, with one subpopulation characterized by high UGDH expression. This subpopulation was shown to possess distinct metabolic features in metabolism-related investigations. The intricate communication modalities among different cell types were effectively demonstrated by means of CellChat. Additionally, a crucial transcription factor, CEBPD, was identified, which demonstrated a pronounced propensity towards tumors and harbored potential tumor-advancing characteristics. Its role in promoting cancer was subsequently verified through in vitro knock-down experiments. Moreover, a prognostic model was also developed, and a risk score was established based on the genes incorporated in the model. Through comparing the prognoses of different UTRS levels, it was determined that the group with a high UTRS had a less favorable prognosis.

Conclusion

These outcomes contributed to the elucidation of the complex interrelationships within the BC tumor microenvironment. By specifically targeting certain subpopulations of TCs, novel treatment strategies could potentially be devised. This study shed light on the direction that future research in BC should take, furnishing valuable information that can be utilized to enhance treatment regimens.

Prognostic implications of ERLncRNAs in ccRCC: a novel risk score model and its association with tumor mutation burden and immune microenvironment

INTRODUCTION/BACKGROUND

The specific role of efferocytosis-related long noncoding RNAs (ERLncRNAs) in Clear Cell Renal Cell Carcinoma (ccRCC) has not been thoroughly examined. This study aims to identify and validate a signature of ERLncRNAs for prognostic prediction and characterization of the immune landscape in individuals with ccRCC.

MATERIALS AND METHODS

Analysis of ccRCC samples was conducted by utilizing clinical and RNA sequencing information obtained from The Cancer Genome Atlas (TCGA). Pearson correlation analysis was utilized to identify lncRNAs associated with efferocytosis, which was then used to create a new prognostic model through univariate Cox regression, Least Absolute Shrinkage and Selection Operator (LASSO) regression, and stepwise multivariate Cox analysis. In order to investigate the biological significance, we performed a functional enrichment analysis to assess how well the model predicts outcomes. Differences in the immune landscape were observed through a comparison of immune cell infiltration, tumor mutational burden (TMB), and tumor microenvironment (TME) characteristics. Following this, drug sensitivity analysis was conducted.

RESULTS

This led to the identification of a unique signature consisting of seven ERLncRNAs (LINC01615, RUNX3-AS1, FOXD2-AS1, AC002070.1, LINC02747, LINC00944, and AC092296.1). Model performance was measured by Kaplan-Meier curves and receiver operating characteristic (ROC) curves. The nomogram and C-index provided additional validation of the strong correlation between the risk signature and clinical decision-making.

CONCLUSION

On the whole, our innovative signature exhibits potential for prognostic prediction and assessment of immunotherapeutic response in patients with ccRCC.

Heterogeneity of cancer-associated fibroblast subpopulations in prostate cancer: Implications for prognosis and immunotherapy

BACKGROUND

Prostate cancer stands as the second most common malignancy among men, notorious for its intricate heterogeneity, especially evident in metastatic disease. This complexity presents substantial challenges in treatment efficacy and patient prognosis.

OBJECTIVE

This study endeavors to elucidate the multifaceted roles of cancer-associated fibroblasts within the tumor microenvironment of prostate cancer, with a focus on their implications for disease prognosis and the potential for novel immunotherapeutic strategies.

METHODS

Leveraging advanced single-cell RNA sequencing technology, we meticulously characterized the diverse CAF subpopulations within prostate cancer samples. Our analysis identified four predominant subsets: C0 IER2+, C1 ABCA8+, C2 ABI3BP+, and C3 MEOX2+. We conducted comprehensive gene expression profiling to construct a robust prognostic model reflecting the clinical relevance of these subpopulations.

RESULTS

C1 ABCA8+ fibroblasts demonstrated heightened proliferative activity, underscoring their pivotal role in fostering tumor growth and metastasis via intricate signaling pathways. In vitro experiments verified that the T transcription factor NFAT5 of C1 ABCA8+ fibroblasts subpopulation was knocked down in LNCaP clone FGC and 22Rv1 cell lines, which was closely related to the proliferation of PC. Moreover, we identified key genes linked to patient outcomes and immune landscape alterations, reinforcing the prognostic significance of CAF characteristics in this context.

CONCLUSION

This investigation illuminates the critical potential of targeting CAFs to augment immunotherapeutic approaches in prostate cancer. Our findings contribute to a deeper understanding of the TME's complexity, advocating for further exploration into CAF-targeted therapies aimed at enhancing treatment responses and ultimately improving patient outcomes.

Single-cell sequencing uncovers the mechanistic role of DAPK1 in glioma and its diagnostic and prognostic implications

Background

We conducted an investigation into the characteristics of single-cell differentiation data in gliomas, with a focus on developing DAPK1-based prognostic markers to predict patient outcomes. Dysregulated expression of DAPK1 has been associated with the invasive behavior of various malignancies, including gliomas. However, the precise role and underlying mechanisms of DAPK1 in gliomas remain inadequately understood.

Methods

We performed analyses on RNA-seq and microarray datasets from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO), in addition to single-cell RNA sequencing (scRNA-seq) data from glioma patients available in GEO. Utilizing the Seurat R package, we identified gene clusters associated with survival from the scRNA-seq data. Prognostic models were developed using LASSO and stepwise regression algorithms. Furthermore, we assessed the predictive potential of these genes within the immune microenvironment and their relevance in immunotherapy contexts.

Results

Our scRNA-seq data analysis revealed 32 distinct cell clusters corresponding to 10 cell types. Through dimensionality reduction and clustering, we identified three glial cell subpopulations based on their differentiation trajectories. DAPK1, serving as a marker gene for the terminal subpopulation, exhibited an association with poor prognosis.

Conclusions

DAPK1-based prognostic models show promise for accurately predicting outcomes in glioblastoma and glioma. An in-depth examination of DAPK1's specific mechanisms in glioblastoma could elucidate its role in immunotherapy response. Targeting the DAPK1 gene may offer therapeutic benefits for glioma patients.

Inhibition of programmed cell death by melanoma cell subpopulations reveals mechanisms of melanoma metastasis and potential therapeutic targets

Melanoma is an aggressive type of skin cancer that arises from melanocytes, the cells responsible for producing skin pigment. In contrast to non-melanoma skin cancers like basal cell carcinoma and squamous cell carcinoma, melanoma is more invasive. Melanoma was distinguished by its rapid progression, high metastatic potential, and significant resistance to conventional therapies. Although it accounted for a small proportion of skin cancer cases, melanoma accounts for the majority of deaths caused by skin cancer due to its ability to invade deep tissues, adapt to diverse microenvironments, and evade immune responses. These unique features highlighted the challenges of treating melanoma and underscored the importance of advanced tools, such as single-cell sequencing, to unravel its biology and develop personalized therapeutic strategies. Thus, we conducted a single-cell analysis of the cellular composition within melanoma tumor tissues and further subdivided melanoma cells into subpopulations. Through analyzing metabolic pathways, stemness genes, and transcription factors (TFs) among cells in different phases (G1, G2/M, and S) as well as between primary and metastatic foci cells, we investigated the specific mechanisms underlying melanoma metastasis. We also revisited the cellular stemness and temporal trajectories of melanoma cell subpopulations, identifying the core subpopulation as C0 SOD3 + Melanoma cells. Our findings revealed a close relationship between the pivotal C0 SOD3 + Melanoma cells subpopulation and oxidative pathways in metastatic tumor tissues. Additionally, we analyzed prognostically relevant differentially expressed genes (DEGs) within the C0 SOD3 + Melanoma cells subpopulation and built a predictive model associated with melanoma outcomes. We selected the gene IGF1 with the highest coefficient (coef) value for further analysis, and experimentally validated its essential function in the proliferation and invasive metastasis of melanoma. In immune infiltration analysis, we discovered the critical roles played by M1/M2 macrophages in melanoma progression and immune evasion. Furthermore, the development and progression of malignant melanoma were closely associated with various forms of programmed cell death (PCD), including apoptosis, autophagic cell death, ferroptosis, and pyroptosis. Melanoma cells often resisted cell death mechanisms, maintaining their growth by inhibiting apoptosis and evading autophagic cell death. Meanwhile, the induction of ferroptosis and pyroptosis was thought to trigger immune responses that helped suppress melanoma dissemination. A deeper understanding of the relationship between melanoma and PCD pathways provided a critical foundation for developing novel targeted therapies, with the potential to enhance melanoma treatment efficacy. These findings contributed to the development of novel prognostic models for melanoma and shed light on research directions concerning melanoma metastasis mechanisms and therapeutic targets.

Characterizing tumor biology and immune microenvironment in high-grade serous ovarian cancer via single-cell RNA sequencing: insights for targeted and personalized immunotherapy strategies

Background

High-grade serous ovarian cancer (HGSOC), the predominant subtype of epithelial ovarian cancer, is frequently diagnosed at an advanced stage due to its nonspecific early symptoms. Despite standard treatments, including cytoreductive surgery and platinum-based chemotherapy, significant improvements in survival have been limited. Understanding the molecular mechanisms, immune landscape, and drug sensitivity of HGSOC is crucial for developing more effective and personalized therapies. This study integrates insights from cancer immunology, molecular profiling, and drug sensitivity analysis to identify novel therapeutic targets and improve treatment outcomes. Utilizing single-cell RNA sequencing (scRNA-seq), the study systematically examines tumor heterogeneity and immune microenvironment, focusing on biomarkers influencing drug response and immune activity, aiming to enhance patient outcomes and quality of life.

Methods

scRNA-seq data was obtained from the GEO database in this study. Differential gene expression was analyzed using gene ontology and gene set enrichment methods. InferCNV identified malignant epithelial cells, while Monocle, Cytotrace, and Slingshot software inferred subtype differentiation trajectories. The CellChat software package predicted cellular communication between malignant cell subtypes and other cells, while pySCENIC analysis was utilized to identify transcription factor regulatory networks within malignant cell subtypes. Finally, the analysis results were validated through functional experiments, and a prognostic model was developed to assess prognosis, immune infiltration, and drug sensitivity across various risk groups.

Results

This study investigated the cellular heterogeneity of HGSOC using scRNA-seq, focusing on tumor cell subtypes and their interactions within the tumor microenvironment. We confirmed the key role of the C2 IGF2+ tumor cell subtype in HGSOC, which was significantly associated with poor prognosis and high levels of chromosomal copy number variations. This subtype was located at the terminal differentiation of the tumor, displaying a higher degree of malignancy and close association with stage IIIC tissue types. The C2 subtype was also associated with various metabolic pathways, such as glycolysis and riboflavin metabolism, as well as programmed cell death processes. The study highlighted the complex interactions between the C2 subtype and fibroblasts through the MK signaling pathway, which may be closely related to tumor-associated fibroblasts and tumor progression. Elevated expression of PRRX1 was significantly connected to the C2 subtype and may impact disease progression by modulating gene transcription. A prognostic model based on the C2 subtype demonstrated its association with adverse prognosis outcomes, emphasizing the importance of immune infiltration and drug sensitivity analysis in clinical intervention strategies.

Conclusion

This study integrates molecular oncology, immunotherapy, and drug sensitivity analysis to reveal the mechanisms driving HGSOC progression and treatment resistance. The C2 IGF2+ tumor subtype, linked to poor prognosis, offers a promising target for future therapies. Emphasizing immune infiltration and drug sensitivity, the research highlights personalized strategies to improve survival and quality of life for HGSOC patients.

Unveiling the NEFH+ malignant cell subtype: Insights from single-cell RNA sequencing in prostate cancer progression and tumor microenvironment interactions

Background

Prostate cancer (PCa) is a multifactorial and heterogeneous disease, ranking among the most prevalent malignancies in men. In 2020, there were 1,414,259 new cases of PCa worldwide, accounting for 7.3% of all malignant tumors. The incidence rate of PCa ranks third, following breast cancer and lung cancer. Patients diagnosed with high-grade PCa frequently present with existing or developing metastases, complicating their treatment and resulting in poorer prognoses, particularly for those with bone metastases. Utilizing single-cell RNA sequencing (scRNA-seq), we identified specific malignant cell subtypes that are closely linked to high-grade PCa. By investigating the mechanisms that govern interactions within the tumor microenvironment (TME), we aim to offer new theoretical insights that can enhance the prevention, diagnosis, and treatment of PCa, ultimately striving to improve patient outcomes and quality of life.

Methods

Data on scRNA-seq was obtained from the GEO database. The gene ontology and gene set enrichment analysis were employed to analyze differential expression genes. Using inferCNV analysis to identify malignant epithelial cells. We subsequently employed Monocle, Cytotrace, and Slingshot packages to infer subtype differentiation trajectories. The cellular communication between malignant cell subtypes and other cells was predicted using the CellChat package. Furthermore, we employed pySCENIC to analyze and identify the regulatory networks of transcription factors (TFs) in malignant cell subtypes. The MDA PCa 2b and VCap cell lines were employed to validate the analysis results through cellular functional experiments. In addition, a risk scoring model was developed to assess the variation in clinical characteristics, prognosis, immune infiltration, immune checkpoint, and drug sensitivity.

Results

A malignant cell subtype in PCa with high expression of NEFH was identified through scRNA-seq analysis. This subtype was situated at the differentiation terminal, exhibited a higher level of malignancy, and exhibited characteristics that were more prone to advanced tumor lesions. In addition, our research underscored the intricate interactions that exist within the TME, particularly the interaction between PTN secreted by this subtype and fibroblasts via the NCL receptor. This interaction may be closely associated with cancer-associated fibroblasts and tumor progression. Subsequently, we determined that the NEFH+ malignant cell subtype was significantly correlated with the TF IRX4. This TF is linked to a worse prognosis in PCa and may affect disease progression by regulating gene transcription. Our conclusions were additionally verified through cellular experiments. Furthermore, the prognostic model we developed demonstrated satisfactory predictive performance, with gene sets from the high NmRS group facilitating tumor progression and deterioration. The analysis of immune infiltration was instrumental in the development of clinical intervention strategies and patient prognosis.

Conclusion

By examining the cellular heterogeneity of a unique NEFH+ malignant cell subtype within the PCa microenvironment, we were able to disclose their reciprocal interaction with disease progression. This offers a novel viewpoint on the diagnosis and treatment of PCa.

Single-cell analysis identified key macrophage subpopulations associated with atherosclerosis

Background

Atherosclerosis is a lipid-driven inflammatory disease characterized by plaque formation in major arteries. These plaques contain lipid-rich macrophages that accumulate through monocyte recruitment, local macrophage differentiation, and proliferation.

Objective

We identify the macrophage subsets that are closely related to atherosclerosis and reveal the key pathways in the progression of atherosclerotic disease.

Materials and methods

In this study, we characterize the single-cell landscape of atherosclerosis, identifying macrophage subsets closely related to the disease and revealing key pathways in its progression. Using analytical methods like CytoTRACE, Monocle2, Slingshot, and CellChat, we study macrophage differentiation and infer cell trajectory.

Results

The 8,417 macrophages were divided into six subtypes, macrophages: C0 C1QC+ macrophages, C1 SPP1+ macrophages, C2 FCN1+ macrophages, C3 IGKC+ macrophages, C4 FCER1A+ macrophages, C5CALD1+ macrophages. The results of gene set enrichment analysis, Monocle2, and Slingshot suggest that C2 FCN1+ macrophages may play an important role in the progression of atherosclerosis. C2 FCN1+ macrophages interact with endothelial cells via CCL, CXCL, APP, and other pathways to regulate the progression of atherosclerosis.

Conclusion

We identify a key macrophage subgroup (C2 FCN1+ macrophages) associated with atherosclerosis, which interacts with endothelial cells via CCL, CXCL, APP, and other pathways to regulate disease progression.

A new perspective on macrophage-targeted drug research: the potential of KDELR2 in bladder cancer immunotherapy

Introduction

Bladder cancer was recognized as one of the most common malignant tumors in the urinary system, and treatment options remained largely limited to conventional surgery, radiotherapy, and chemotherapy, which limited patient benefits.

Methods

Researchers constructed an RNA transcriptome map of bladder cancer by integrating single-cell RNA sequencing and clinical data, identifying potential molecular targets for diagnosis and treatment. We also verified the antitumor activity of the target through in vitro experiment.

Results

A distinct tumor cell subpopulation characterized by elevated S100A8 expression exhibited high copy number variation, high stemness, and low differentiation. It interacted with myeloid cells via the MIF-(CD74+CD44) and MIF-(CD74+CXCR4) signaling pathways. This study underscored KDELR2's role in promoting cell proliferation, invasion, and migration, providing new therapeutic insights. Prognostic analysis revealed that KDELR2 correlated with poor survival, higher immune scores, and increased macrophage infiltration.

Discussion

The findings suggested that patients with high KDELR2 expression might benefit from immune checkpoint therapy. KDELR2 was also shown to enhance bladder cancer cell proliferation, invasion, and migration, highlighting it as a promising target for macrophage-focused drug development.

Single-cell RNA sequencing reveals key molecular drivers and immune landscape in uveal melanoma: implications for targeted therapy and prognostic modeling

Background

Uveal melanoma (UM), arising from melanocytes in the choroid, accounts for 3% to 5% of all melanocytic tumors and over 70% of intraocular malignancies. Despite effective local treatments, metastasis remains a significant challenge, with more than half of patients developing metastatic disease within ten years. Conventional therapies often yield poor outcomes, highlighting the urgent need for novel therapeutic strategies to enhance survival and prognosis for UM patients.

Methods

We conducted a detailed analysis of the GSE139829 dataset, focusing on scRNA-seq data from eight primary UM patients and three with metastatic disease. Through clustering and marker gene expression analyses, we identified distinct subtypes of UM tumor cells and examined their transcriptional, metabolic, and intercellular communication profiles. We developed a novel prognostic model, PCOLCE TCs Risk Score (PTRS), centered on the C5 PCOLCE+ tumor cells, which was validated through in vitro functional assays. Additionally, we performed immune infiltration and metabolic pathway analyses to elucidate tumor-immune interactions and their clinical significance.

Results

We identified eight distinct cell types in UM and classified tumor subpopulations into six subgroups. The C5 PCOLCE+ TCs subpopulation was highlighted as crucial in UM malignancy, demonstrating high differentiation potential and a significant role in tumor progression. CellChat analysis revealed substantial communication between C5 PCOLCE+ TCs and fibroblasts, suggesting their involvement in tumor growth and extracellular matrix remodeling. Metabolic pathway analysis indicated enhanced oxidative phosphorylation and glutathione metabolism in this subpopulation. Additionally, we developed a PTRS model based on C5 PCOLCE+ TCs, identifying CITED1 as a high-risk gene that promotes UM cell proliferation, invasion, and migration in vitro.

Conclusion

This study provides insights into UM metastasis via single-cell analysis, identifying C5 PCOLCE+ TCs as key malignancy drivers associated with oxidative phosphorylation and immune interactions. Our PTRS model highlights CITED1 as a high-risk gene that promotes UM cell proliferation, paving the way for new prognostic models and therapeutic targets to enhance patient outcomes.

Tanshinone IIA Protects Ischemia/Reperfusion-Induced Cardiomyocyte Injury by Inhibiting the HAS2/FGF9 Axis

Purpose

This study aimed to investigate the impacts of tanshinone IIA (Tan IIA) on ischemia/reperfusion (I/R)-induced cardiomyocyte injury in coronary heart disease (CHD), and to determine whether Tan IIA regulates myocardial cell injury induced by I/R through the Hyaluronan Synthase 2/fibroblast growth factor 9 (HAS2/FGF9) axis.

Methods

Weighted gene co-expression network analysis (WGCNA) of the GSE23561 microarray dataset determined gene modules linked to CHD. The key genes were further explored through differential expression and protein-protein interaction (PPI) network analyses. Human AC16 cardiomyocytes were treated with Tan IIA, HAS2 knockdown, and FGF9 overexpression and they were exposed to normoxic, hypoxic, and I/R environments. Cell viability, apoptosis, gene/protein expression, and markers of oxidative stress were evaluated in vitro.

Results

The turquoise module was significantly correlated with CHD and HAS2 was identified as a hub gene. Under hypoxic conditions, Tan IIA exhibited a dose-dependent cardioprotective effect. Tan IIA ameliorated I/R-induced cellular injury, as evidenced by increased cell viability, decreased apoptosis, and regulation of key proteins (PCNA, Bax). After I/R conditions, knockdown of HAS2 increased cell viability and reduced apoptosis, whereas overexpression of FGF9 reversed these effects. Notably, HAS2 knockdown also ameliorated I/R-induced increases in inflammatory cytokines and oxidative stress, and synergistic protection was provided by combined treatment with FGF9 and Tan IIA.

Conclusion

Taken together, our findings confirm that Tan IIA protects cardiomyocytes from I/R-induced injury by controlling the HAS2/FGF9 axis. These findings reveal the potential therapeutic significance of Tan IIA in alleviating CHD-related myocardial dysfunction.

Unraveling the ecological landscape of mast cells in esophageal cancer through single-cell RNA sequencing

Background

Esophageal cancer (EC) is a major health issue, ranking seventh in incidence and sixth in mortality worldwide. Despite advancements in multidisciplinary treatment approaches, the 5-year survival rate for EC remains low at 21%. Challenges in EC treatment arise from late-stage diagnosis, high malignancy, and poor prognosis. Understanding the tumor microenvironment is critical, as it includes various cellular and extracellular components that influence tumor behavior and treatment response. Mast cells (MCs), as tissue-resident immune cells, play dual roles in tumor dynamics. High-throughput single-cell RNA sequencing offers a powerful tool for analyzing tumor heterogeneity and immune interactions, although its application in EC is limited.

Methods

In this study, we investigated the immune microenvironment of EC using single-cell RNA sequencing and established a comprehensive immune profile. We also performed analysis of upstream transcription factors and downstream pathway enrichment to further comprehensively decipher MCs in EC. Besides, we performed knockdown experiments to explore the role of epidermal growth factor receptor (EGFR) signaling pathway in MCs-tumor cell interactions, highlighting its potential as a prognostic marker. Finally, we constructed a prognostic model for EC, which provided valuable suggestions for the diagnosis and prognosis of EC.

Results

Our analysis identified 11 major cell types, of which MCs were particularly present in pericarcinoma tissues. Further grouping of the 5,001 MCs identified 8 distinct subtypes, including SRSF7-highly expressed MCs, which showed strong tumor preference and potential tumor-promoting properties. Moreover, we identified the key signaling receptor EGFR and validated it by in vitro knockdown experiments, demonstrating its cancer-promoting effects. In addition, we established an independent prognostic indicator, SRSF7+ MCs risk score (SMRS), which showed a correlation between high SMRS group and poor prognosis.

Conclusion

These findings illuminate the complex interactions within the tumor microenvironment of EC and suggest that targeting specific MCs subtypes, particularly via the EGFR signaling pathway, may present novel therapeutic strategies. This study establishes a comprehensive immune map of EC, offering insights for improved treatment approaches.

An MRI-based radiomics nomogram to predict progression-free survival in patients with endometrial cancer

Aim: This study aimed to explore the importance of an MRI-based radiomics nomogram in predicting the progression-free survival (PFS) of endometrial cancer.Methods: Based on clinicopathological and radiomic characteristics, we established three models (clinical, radiomics and combined model) and developed a nomogram for the combined model. The Kaplan-Meier method was utilized to evaluate the association between nomogram-based risk scores and PFS.Results: The nomogram had a strong predictive ability in calculating PFS with areas under the curve (ROC) of 0.905 and 0.901 at 1 and 3 years, respectively. The high-risk groups identified by the nomogram-based scores had shorter PFS compared with the low-risk groups.Conclusion: The radiomics nomogram has the potential to serve as a noninvasive imaging biomarker for predicting individual PFS of endometrial 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.

Elucidating the role of tumor-associated ALOX5+ mast cells with transformative function in cervical cancer progression via single-cell RNA sequencing

BACKGROUND

Cervical cancer (CC) is the fourth most common malignancy among women globally and serves as the main cause of cancer-related deaths among women in developing countries. The early symptoms of CC are often not apparent, with diagnoses typically made at advanced stages, which lead to poor clinical prognoses. In recent years, numerous studies have shown that there is a close relationship between mast cells (MCs) and tumor development. However, research on the role MCs played in CC is still very limited at that time. Thus, the study conducted a single-cell multi-omics analysis on human CC cells, aiming to explore the mechanisms by which MCs interact with the tumor microenvironment in CC. The goal was to provide a scientific basis for the prevention, diagnosis, and treatment of CC, with the hope of improving patients' prognoses and quality of life.

METHOD

The present study acquired single-cell RNA sequencing data from ten CC tumor samples in the ArrayExpress database. Slingshot and AUCcell were utilized to infer and assess the differentiation trajectory and cell plasticity of MCs subpopulations. Differential expression analysis of MCs subpopulations in CC was performed, employing Gene Ontology, gene set enrichment analysis, and gene set variation analysis. CellChat software package was applied to predict cell communication between MCs subpopulations and CC cells. Cellular functional experiments validated the functionality of TNFRSF12A in HeLa and Caski cell lines. Additionally, a risk scoring model was constructed to evaluate the differences in clinical features, prognosis, immune infiltration, immune checkpoint, and functional enrichment across various risk scores. Copy number variation levels were computed using inference of copy number variations.

RESULT

The obtained 93,524 high-quality cells were classified into ten cell types, including T_NK cells, endothelial cells, fibroblasts, smooth muscle cells, epithelial cells, B cells, plasma cells, MCs, neutrophils, and myeloid cells. Furthermore, a total of 1,392 MCs were subdivided into seven subpopulations: C0 CTSG+ MCs, C1 CALR+ MCs, C2 ALOX5+ MCs, C3 ANXA2+ MCs, C4 MGP+ MCs, C5 IL32+ MCs, and C6 ADGRL4+ MCs. Notably, the C2 subpopulation showed close associations with tumor-related MCs, with Slingshot results indicating that C2 subpopulation resided at the intermediate-to-late stage of differentiation, potentially representing a crucial transition point in the benign-to-malignant transformation of CC. CNVscore and bulk analysis results further confirmed the transforming state of the C2 subpopulation. CellChat analysis revealed TNFRSF12A as a key receptor involved in the actions of C2 ALOX5+ MCs. Moreover, in vitro experiments indicated that downregulating the TNFRSF12A gene may partially inhibit the development of CC. Additionally, a prognosis model and immune infiltration analysis based on the marker genes of the C2 subpopulation provided valuable guidance for patient prognosis and clinical intervention strategies.

CONCLUSIONS

We first identified the transformative tumor-associated MCs subpopulation C2 ALOX5+ MCs within CC, which was at a critical stage of tumor differentiation and impacted the progression of CC. In vitro experiments confirmed the inhibitory effect of knocking down the TNFRSF12A gene on the development of CC. The prognostic model constructed based on the C2 ALOX5+MCs subset demonstrated excellent predictive value. These findings offer a fresh perspective for clinical decision-making in CC.

Disulfidptosis-Related LncRNA Signatures for Prognostic Prediction in Kidney Renal Clear Cell Carcinoma

INTRODUCTION BACKGROUND

Disulfidptosis is a prevalent apoptotic mechanism, intrinsically linked to cancer prognosis. However, the specific involvement of disulfidptosis-related long non-coding RNA (DRLncRNAs) in Kidney renal clear cell carcinoma (KIRC) remains incompletely understood. This study aims to elucidate the potential prognostic significance of disulfidptosis-related LncRNAs in KIRC.

MATERIALS AND METHODS

Expression profiles and clinical data of KIRC patients were retrieved from the TCGA database to discern differentially expressed DRLncRNAs correlated with overall survival. Cox univariate analysis, Lasso Regression, and Cox multivariate analysis were used to construct a clinical prediction model.

RESULTS

Six signatures, namely FAM83C.AS1, AC136475.2, AC121338.2, AC026401.3, AC254562.3, and AC000050.2, were established to evaluate overall survival (OS) in the context of Kidney renal clear cell carcinoma (KIRC) in this study. Survival analysis and ROC curves demonstrated the strong predictive performance of the associated signature. The nomogram exhibited accurate prognostic predictions for overall patient survival, offering substantial clinical utility. Gene set enrichment analysis revealed that risk signals were enriched in various immune-related pathways. Furthermore, the risk features exhibited significant correlations with immune cells, immune function, immune cell infiltration, and immune checkpoints.

CONCLUSION

This study has unveiled, for the first time, six disulfdptosis-related LncRNA signatures, laying a solid foundation for enhanced and precise prognostic predictions in KIRC.

Effects and mechanisms of puerarin against neuroblastoma: insights from bioinformatics and in vitro experiments

BACKGROUND

Neuroblastoma, a prevalent solid tumor in children, often manifests with hidden onset sites, rapid growth, and high metastatic potential. The prognosis for children with high-risk neuroblastoma remains poor, highlighting the urgent need for novel prognostic models and therapeutic avenues. In recent years, puerarin, as a kind of small molecule drug extracted from Chinese medicine Pueraria lobata, has demonstrated significant anticancer effects on various cancer cell types. In this study, through bioinformatics analysis and in vitro experiments, the potential and mechanism of puerarin in the treatment of neuroblastoma were investigated, and a prognostic model was established.

METHODS

A total of 9 drug-disease related targets were observed by constructing a database of drug targets and disease genes. Besides, GO and KEGG enrichment analysis was performed to explore the potential mechanism of its therapeutic effect. To construct the prognostic model, risk regression analysis and LASSO analysis were carried out for validation. Finally, the prognostic genes were identified. Parachute test and immunofluorescence staining were performed to verify the potential mechanism of puerarin in neuroblastoma treatment.

RESULTS

Three prognostic genes, i.e., BIRC5, TIMP2 and CASP9, were identified. In vitro studies verified puerarin's impact on BIRC5, TIMP2, and CASP9 expression, inhibiting proliferation in neuroblastoma SH-SY5Y cells. Puerarin disrupts the cytoskeleton, boosts gap junctional communication, curtailing invasion and migration, and induces mitochondrial damage in SH-SY5Y cells.

CONCLUSIONS

Based on network pharmacology and bioinformatics analysis, combined with in vitro experimental verification, puerarin was hereby observed to enhance GJIC in neuroblastoma, destroy cytoskeleton and thus inhibit cell invasion and migration, cause mitochondrial damage of tumor cells, and inhibit cell proliferation. Overall, puerarin, as a natural medicinal compound, does hold potential as a novel therapy for neuroblastoma.

Predicting prognosis in colorectal cancer patients with curative resection using albumin, lymphocyte count and RAS mutations

Colorectal cancer (CRC) poses a significant global health challenge, demanding reliable prognostic tools to guide treatment decisions. This study introduces a novel prognostic scoring system, the albumin-total lymphocyte count-RAS index (ALRI), integrating serum albumin, lymphocyte count, and RAS gene mutations. A cohort of 445 stage I-III CRC patients undergoing curative resection was analyzed, revealing ALRI's association with clinicopathological factors, including age, tumor location, and invasion depth. The ALRI demonstrated superior prognostic value, with a cutoff value of 2 distinguishing high and low-risk groups. The high-ALRI group exhibited elevated rates of recurrence. Univariate and multivariate analyses identified ALRI as an independent predictor for both 5 year recurrence-free survival (RFS) and overall survival (OS). Kaplan-Meier curves illustrated significant differences in RFS and OS between high and low-ALRI groups, emphasizing ALRI's potential as a prognostic marker. Importantly, ALRI outperformed existing nutritional indices, such as controlling nutritional status and neutrophil-to-lymphocyte ratio, in predicting overall survival. The study underscores the comprehensive insight provided by ALRI, combining inflammatory, nutritional, and genetic information for robust prognostication in CRC patients. This user-friendly tool demonstrates promise for preoperative prognosis and personalized treatment strategies, emphasizing the crucial role of inflammation and nutrition in CRC outcomes.

Integrative Single-Cell Analysis of Cardiomyopathy Identifies Differences in Cell Stemness and Transcriptional Regulatory Networks among Fibroblast Subpopulations

Background

Cardiomyopathy encompasses a broad spectrum of diseases affecting myocardial tissue, characterized clinically by abnormalities in cardiac structure, heart failure, and/or arrhythmias. Clinically heterogeneous, major types include dilated cardiomyopathy (DCM), hypertrophic cardiomyopathy (HCM), restrictive cardiomyopathy (RM), ischemic cardiomyopathy (ICM), among which DCM is more prevalent, while ICM exhibits higher incidence and mortality rates. Myocardial injury during cardiomyopathy progression may lead to myocardial fibrosis. Failure to intervene early and inhibit the process of myocardial fibrosis may culminate in heart failure. Cardiac fibroblasts constitute crucial cellular components determining the extent and quality of myocardial fibrosis, with various subpopulations exerting diverse roles in cardiomyopathy progression. Despite this, understanding of the cellular plasticity and transcriptional regulatory networks of cardiac fibroblasts in cardiomyopathy remains limited. Therefore, in this study, we conducted comprehensive single-cell analysis of cardiac fibroblasts in cardiomyopathy to explore differences in cellular plasticity and transcriptional regulatory networks among fibroblast subpopulations, with the aim of providing as many useful references as possible for the diagnosis, prognosis, and treatment of cardiomyopathy.

Materials and Methods

Cells with mitochondrial gene expression comprising >20% of total expressed genes were excluded. Differential expression genes (DEGs) and stemness genes within cardiac fibroblast subpopulations were subjected to Gene Ontology (GO) analysis of biological processes (BP) and AUCell analysis. Monocle software was employed to analyze the pseudo-temporal trajectory of cardiac fibroblasts in cardiomyopathy. Additionally, the Python package SCENIC was utilized to assess enrichment of transcription factors and activity of regulators within cardiac fibroblast subpopulations in cardiomyopathy.

Results

Following batch effect correction, 179,927 cells were clustered into 32 clusters, designated as T_NK cells, endothelial cells, myeloid cells, fibroblasts, pericytes, SMCs, CMs, proliferating cells, EndoCs, and EPCs. Among them, 8148 fibroblasts were further subdivided into 4 subpopulations, namely C0 THBS4+ Fibroblasts, C1 LINC01133+ Fibroblasts, C2 FGF7+ Fibroblasts, and C3 AGT + Fibroblasts. Results from GO_BP and AUCell analyses suggest that C3 AGT + Fibroblasts may be associated with immune response activation, protein transport, and myocardial contractile function, correlating with disease progression in cardiomyopathy. Transcription factor enrichment analysis indicates that FOS is the most significant TF in C3 AGT + Fibroblasts, also associated with the M1 module, possibly implicated in protein hydrolysis, intracellular DNA replication, and cell proliferation. Moreover, correlation analysis of transcriptional regulatory activity between fibroblast subpopulations reveals a more pronounced heterogeneity within C3 AGT + Fibroblasts in cardiomyopathy.

Conclusion

C3 AGT + Fibroblasts exhibit increased sensitivity towards adverse outcomes in cardiomyopathy, such as myocardial fibrosis and impaired cardiac contractile function, compared to other cardiac fibroblast subpopulations. The differential cellular plasticity and transcriptional regulatory activity between C3 AGT + Fibroblasts and other subgroups offer new perspectives for targeting fibroblast subpopulation activity to treat cardiomyopathy. Additionally, stemness genes EPAS1 and MYC, along with the regulator FOS, may play roles in modulating the biological processes of cardiac fibroblasts in cardiomyopathy.

Identification of necroptosis-related gene signatures for predicting the prognosis of ovarian cancer

Ovarian cancer (OC) is one of the most prevalent and fatal malignant tumors of the female reproductive system. Our research aimed to develop a prognostic model to assist inclinical treatment decision-making.Utilizing data from The Cancer Genome Atlas (TCGA) and copy number variation (CNV) data from the University of California Santa Cruz (UCSC) database, we conducted analyses of differentially expressed genes (DEGs), gene function, and tumor microenvironment (TME) scores in various clusters of OC samples.Next, we classified participants into low-risk and high-risk groups based on the median risk score, thereby dividing both the training group and the entire group accordingly. Overall survival (OS) was significantly reduced in the high-risk group, and two independent prognostic factors were identified: age and risk score. Additionally, three genes-C-X-C Motif Chemokine Ligand 10 (CXCL10), RELB, and Caspase-3 (CASP3)-emerged as potential candidates for an independent prognostic signature with acceptable prognostic value. In Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses, pathways related to immune responses and inflammatory cell chemotaxis were identified. Cellular experiments further validated the reliability and precision of our findings. In conclusion, necroptosis-related genes play critical roles in tumor immunity, and our model introduces a novel strategy for predicting the prognosis of OC patients.

Development and assessment of an RNA editing-based risk model for the prognosis of cervical cancer patients

RNA editing, as an epigenetic mechanism, exhibits a strong correlation with the occurrence and development of cancers. Nevertheless, few studies have been conducted to investigate the impact of RNA editing on cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC). In order to study the connection between RNA editing and CESC patients' prognoses, we obtained CESC-related information from The Cancer Genome Atlas (TCGA) database and randomly allocated the patients into the training group or testing group. An RNA editing-based risk model for CESC patients was established by Cox regression analysis and least absolute shrinkage and selection operator (LASSO). According to the median score generated by this RNA editing-based risk model, patients were categorized into subgroups with high and low risks. We further constructed the nomogram by risk scores and clinical characteristics and analyzed the impact of RNA editing levels on host gene expression levels and adenosine deaminase acting on RNA. Finally, we also compared the biological functions and pathways of differentially expressed genes (DEGs) between different subgroups by enrichment analysis. In this risk model, we screened out 6 RNA editing sites with significant prognostic value. The constructed nomogram performed well in forecasting patients' prognoses. Furthermore, the level of RNA editing at the prognostic site exhibited a strong correlation with host gene expression. In the high-risk subgroup, we observed multiple biological functions and pathways associated with immune response, cell proliferation, and tumor progression. This study establishes an RNA editing-based risk model that helps forecast patients' prognoses and offers a new understanding of the underlying mechanism of RNA editing in CESC.

Combining bulk and scRNA-seq to explore the molecular mechanisms governing the distinct efferocytosis activities of a macrophage subpopulation in PDAC

Pancreatic ductal adenocarcinoma (PDAC), a very aggressive tumour, is currently the third leading cause of cancer-related deaths. Unfortunately, many patients face the issue of inoperability at the diagnostic phase leading to a quite dismal prognosis. The onset of metastatic processes has a crucial role in the elevated mortality rates linked to PDAC. Individuals with metastatic advances receive only palliative therapy and have a grim prognosis. It is essential to carefully analyse the intricacies of the metastatic process to enhance the prognosis for individuals with PDAC. Malignancy development is greatly impacted by the process of macrophage efferocytosis. Our current knowledge about the complete range of macrophage efferocytosis activities in PDAC and their intricate interactions with tumour cells is still restricted. This work aims to resolve communication gaps and pinpoint the essential transcription factor that is vital in the immunological response of macrophage populations. We analysed eight PDAC tissue samples sourced from the gene expression omnibus. We utilized several software packages such as Seurat, DoubletFinder, Harmony, Pi, GSVA, CellChat and Monocle from R software together with pySCENIC from Python, to analyse the single-cell RNA sequencing (scRNA-seq) data collected from the PDAC samples. This study involved the analysis of a comprehensive sample of 22,124 cells, which were classified into distinct cell types. These cell types encompassed endothelial and epithelial cells, PDAC cells, as well as various immune cells, including CD4+ T cells, CD8+ T cells, NK cells, B cells, plasma cells, mast cells, monocytes, DC cells and different subtypes of macrophages, namely C0 macrophage TGM2+, C1 macrophage PFN1+, C2 macrophage GAS6+ and C3 macrophage APOC3+. The differentiation between tumour cells and epithelial cells was achieved by the implementation of CopyKat analysis, resulting in the detection and categorization of 1941 PDAC cells. The amplification/deletion patterns observed in PDAC cells on many chromosomes differ significantly from those observed in epithelial cells. The study of Pseudotime Trajectories demonstrated that the C0 macrophage subtype expressing TGM2+ had the lowest level of differentiation. Additionally, the examination of gene set scores related to efferocytosis suggested that this subtype displayed higher activity during the efferocytosis process compared to other subtypes. The most active transcription factors for each macrophage subtype were identified as BACH1, NFE2, TEAD4 and ARID3A. In conclusion, the examination of human PDAC tissue samples using immunofluorescence analysis demonstrated the co-localization of CD68 and CD11b within regions exhibiting the presence of keratin (KRT) and alpha-smooth muscle actin (α-SMA). This observation implies a spatial association between macrophages, fibroblasts, and epithelial cells. There is variation in the expression of efferocytosis-associated genes between C0 macrophage TGM2+ and other macrophage cell types. This observation implies that the diversity of macrophage cells might potentially influence the metastatic advancement of PDAC. Moreover, the central transcription factor of different macrophage subtypes offers a promising opportunity for targeted immunotherapy in the treatment of PDAC.

Deciphering the molecular landscape: integrating single-cell transcriptomics to unravel myofibroblast dynamics and therapeutic targets in clear cell renal cell carcinomas

Background

Clear cell renal cell carcinomas (ccRCCs) epitomize the most formidable clinical subtype among renal neoplasms. While the impact of tumor-associated fibroblasts on ccRCC progression is duly acknowledged, a paucity of literature exists elucidating the intricate mechanisms and signaling pathways operative at the individual cellular level.

Methods

Employing single-cell transcriptomic analysis, we meticulously curated UMAP profiles spanning substantial ccRCC populations, delving into the composition and intrinsic signaling pathways of these cohorts. Additionally, Myofibroblasts were fastidiously categorized into discrete subpopulations, with a thorough elucidation of the temporal trajectory relationships between these subpopulations. We further probed the cellular interaction pathways connecting pivotal subpopulations with tumors. Our endeavor also encompassed the identification of prognostic genes associated with these subpopulations through Bulk RNA-seq, subsequently validated through empirical experimentation.

Results

A notable escalation in the nFeature and nCount of Myofibroblasts and EPCs within ccRCCs was observed, notably enriched in oxidation-related pathways. This phenomenon is postulated to be closely associated with the heightened metabolic activities of Myofibroblasts and EPCs. The Myofibroblasts subpopulation, denoted as C3 HMGA1+ Myofibroblasts, emerges as a pivotal subset, displaying low differentiation and positioning itself at the terminal point of the temporal trajectory. Intriguingly, these cells exhibit a high degree of interaction with tumor cells through the MPZ signaling pathway network, suggesting that Myofibroblasts may facilitate tumor progression via this pathway. Prognostic genes associated with C3 were identified, among which TUBB3 is implicated in potential resistance to tumor recurrence. Finally, experimental validation revealed that the knockout of the key gene within the MPZ pathway, MPZL1, can inhibit tumor activity, proliferation, invasion, and migration capabilities.

Conclusion

This investigation delves into the intricate mechanisms and interaction pathways between Myofibroblasts and ccRCCs at the single-cell level. We propose that targeting MPZL1 and the oxidative phosphorylation pathway could serve as potential key targets for treating the progression and recurrence of ccRCC. This discovery paves the way for new directions in the treatment and prognosis diagnosis of ccRCC in the future.

Investigating the mechanisms of drug resistance and prognosis in ovarian cancer using single-cell RNA sequencing and bulk RNA sequencing

Ovarian cancer stands as a prevalent malignancy within the realm of gynecology, and the emergence of resistance to chemotherapeutic agents remains a pivotal impediment to both prognosis and treatment. Through a single-cell level investigation, we scrutinize the drug resistance and mitotic activity of the core tumor cells in ovarian cancer. Our study revisits the interrelationships and temporal trajectories of distinct epithelial cells (EPCs) subpopulations, while identifying genes associated with ovarian cancer prognosis. Notably, our findings establish a strong association between the drug resistance of EPCs and oxidative phosphorylation pathways. Subsequently, through subpopulation and temporal trajectory analysis, we confirm the intermediate position of EPCs subpopulation C0. Furthermore, we delve into the immunological functions and differentially expressed genes associated with the prognosis of C0, shedding light on the potential for constructing novel ovarian cancer prognosis models and identifying new therapeutic targets.

Unraveling the intricacies of glioblastoma progression and recurrence: insights into the role of NFYB and oxidative phosphorylation at the single-cell level

Background

Glioblastoma (GBM), with its high recurrence and mortality rates, makes it the deadliest neurological malignancy. Oxidative phosphorylation is a highly active cellular pathway in GBM, and NFYB is a tumor-associated transcription factor. Both are related to mitochondrial function, but studies on their relationship with GBM at the single-cell level are still scarce.

Methods

We re-analyzed the single-cell profiles of GBM from patients with different subtypes by single-cell transcriptomic analysis and further subdivided the large population of Glioma cells into different subpopulations, explored the interrelationships and active pathways among cell stages and clinical subtypes of the populations, and investigated the relationship between the transcription factor NFYB of the key subpopulations and GBM, searching for the prognostic genes of GBM related to NFYB, and verified by experiments.

Results

Glioma cells and their C5 subpopulation had the highest percentage of G2M staging and rGBM, which we hypothesized might be related to the higher dividing and proliferating ability of both Glioma and C5 subpopulations. Oxidative phosphorylation pathway activity is elevated in both the Glioma and C5 subgroup, and NFYB is a key transcription factor for the C5 subgroup, suggesting its possible involvement in GBM proliferation and recurrence, and its close association with mitochondrial function. We also identified 13 prognostic genes associated with NFYB, of which MEM60 may cause GBM patients to have a poor prognosis by promoting GBM proliferation and drug resistance. Knockdown of the NFYB was found to contribute to the inhibition of proliferation, invasion, and migration of GBM cells.

Conclusion

These findings help to elucidate the key mechanisms of mitochondrial function in GBM progression and recurrence, and to establish a new prognostic model and therapeutic target based on NFYB.

Decoding the tumor microenvironment and molecular mechanism: unraveling cervical cancer subpopulations and prognostic signatures through scRNA-Seq and bulk RNA-seq analyses

Background

Cervical carcinoma (CC) represents a prevalent gynecological neoplasm, with a discernible rise in prevalence among younger cohorts observed in recent years. Nonetheless, the intrinsic cellular heterogeneity of CC remains inadequately investigated.

Methods

We utilized single-cell RNA sequencing (scRNA-seq) transcriptomic analysis to scrutinize the tumor epithelial cells derived from four specimens of cervical carcinoma (CC) patients. This method enabled the identification of pivotal subpopulations of tumor epithelial cells and elucidation of their contributions to CC progression. Subsequently, we assessed the influence of associated molecules in bulk RNA sequencing (Bulk RNA-seq) cohorts and performed cellular experiments for validation purposes.

Results

Through our analysis, we have discerned C3 PLP2+ Tumor Epithelial Progenitor Cells as a noteworthy subpopulation in cervical carcinoma (CC), exerting a pivotal influence on the differentiation and progression of CC. We have established an independent prognostic indicator-the PLP2+ Tumor EPCs score. By stratifying patients into high and low score groups based on the median score, we have observed that the high-score group exhibits diminished survival rates compared to the low-score group. The correlations observed between these groups and immune infiltration, enriched pathways, single-nucleotide polymorphisms (SNPs), drug sensitivity, among other factors, further underscore their impact on CC prognosis. Cellular experiments have validated the significant impact of ATF6 on the proliferation and migration of CC cell lines.

Conclusion

This study enriches our comprehension of the determinants shaping the progression of CC, elevates cognizance of the tumor microenvironment in CC, and offers valuable insights for prospective CC therapies. These discoveries contribute to the refinement of CC diagnostics and the formulation of optimal therapeutic approaches.