Construction of N-7 methylguanine-related mRNA prognostic model in uterine corpus endometrial carcinoma based on multi-omics data and immune-related analysis

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.

SCUBE1 Promotes Gestational Diabetes Mellitus: A Bioinformatics and Experimental Investigation

Gestational diabetes mellitus (GDM) is one of the most common metabolic diseases in pregnant women, posing significant risks to the life and health of both mothers and fetuses. With improving living standards, the incidence of GDM is increasing rapidly. Therefore, understanding the underlying mechanism of GDM is of paramount importance. We downloaded two datasets from the Gene Expression Omnibus (GEO) database, containing sequencing data specifically related to "gestational diabetes" and "placenta". By merging these two datasets, a mRNA expression dataset was obtained and subjected to bioinformatics analyses. To screen out corresponding genes, differential analysis and weighted correlation network analysis (WGCNA) were carried out. Lasso, support vector machine and random forest analyses were subsequently performed for identifying key genes from the differentially expressed genes (DEGs) jointly screened out through differential analysis and WGCNA. Afterwards, immunoinfiltration and correlation analysis were performed to screen immune cells that play a role in disease progression and explore the correlation between the screened key genes and immune cells, after which Western Blot, quantitative real-time polymerase chain reaction, Immunohistochemistry, methyl thiazolyl tetrazolium, flow cytometry, scratch and Transwell assays were, respectively, performed for verification. For further verification, we found that the expression levels of MAP6D1 and SCUBE1 in embryonic tissues of GDM patients was higher compared to those of healthy pregnant women, which was consistent with the results of bioinformatics analysis. Consequently, SCUBE1 was selected for follow-up experiment. In order to explore the role of SCUBE1 in the development of GDM, we treated the trophoblastic cells HTR-8/SVneo with high glucose, and on this basis downregulated the expression of SCUBE1. Through further analysis, we observed that SCUBE1 had a role in reducing cell activity, migration and invasion, and promoting cell apoptosis. In summary, SCUBE1 promotes the development of GDM by increasing cell apoptosis and reducing cell activity, migration, and invasion.

EXT1 and Its Methylation Involved in the Progression of Uterine Corpus Endometrial Carcinoma Pathogenesis

Uterine corpus endometrial carcinoma (UCEC) is one of the most common gynecologic tumors. Due to the high recurrence and metastasis of UCEC, it is crucial for patients to find new biomarkers for diagnosis and therapy. In this study, R software and the TCGA database were used to screen candidate UCEC predictive markers. Western blot and RT-qPCR were performed to detect protein and mRNA expression of EXT1 in UCEC cell lines. In addition, MTT assay, flow cytometry, transwell assay, and wound healing assay were conducted to assess the cell viability, apoptosis, invasion, and migration in UCEC cells. Overlap-extension PCR technique was employed to construct the vector targeting the deletion of the methylated segment of EXT1. The results showed that a total of 11 candidate genes were obtained and EXT1 was identified as a potential target. The expression and methylation levels of EXT1 were both increased in UCEC tissues and cell lines, as well as elevated EXT1 was closely related to the poor prognosis of patients. Besides, the knockdown of EXT1 significantly inhibited the malignant biological behaviors in UCEC cells. Additionally, the current study also found that the deletion of 1559-2146 bp CpG island segment upregulated EXT1 expression and promoted malignant biological behaviors in UCEC cells. Furthermore, the presence of m7G RNA methylation in UCEC cells also was found. In conclusion, the methylation of EXT1 influenced the gene expression, thereby affecting the malignant biological behaviors in UCEC cells and regulating the pathological progression of UCEC.

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.

MAZ-mediated tumor progression and immune evasion in hormone receptor-positive breast cancer: Targeting tumor microenvironment and PCLAF+ subtype-specific therapy

BACKGROUND

Breast cancer had been the most frequently diagnosed cancer among women, making up nearly one-third of all female cancers. Hormone receptor-positive breast cancer (HR+BC) was the most prevalent subtype of breast cancer and exhibited significant heterogeneity. Despite advancements in endocrine therapies, patients with advanced HR+BC often faced poor outcomes due to the development of resistance to treatment. Understanding the molecular mechanisms behind this resistance, including tumor heterogeneity and changes in the tumor microenvironment, was crucial for overcoming resistance, identifying new therapeutic targets, and developing more effective personalized treatments.

METHODS

The study utilized single-cell RNA sequencing (scRNA-seq) data sourced from the Gene Expression Omnibus database and The Cancer Genome Atlas to analyze HR+BC and identify key cellular characteristics. Cell type identification was achieved through Seurat's analytical tools, and subtype differentiation trajectories were inferred using Slingshot. Cellular communication dynamics between tumor cell subtypes and other cells were analyzed with the CellChat. The pySCENIC package was utilized to analyze transcription factors regulatory networks in the identified tumor cell subtypes. The results were verified by in vitro experiments. A risk scoring model was developed to assess patient outcomes.

RESULTS

This study employed scRNA-seq to conduct a comprehensive analysis of HR+BC tumor subtypes, identifying the C3 PCLAF+ tumor cells subtype, which demonstrated high proliferation and differentiation potential. C3 PCLAF+ tumor cells subtype was found to be closely associated with cancer-associated fibroblasts through the MK signaling pathway, facilitating tumor progression. Additionally, we discovered that MAZ was significantly expressed in C3 PCLAF+ tumor cells subtype, and in vitro experiments confirmed that MAZ knockdown inhibited tumor growth, accentuating its underlying ability as a therapeutic target. Furthermore, we developed a novel prognostic model based on the expression profile of key prognostic genes within the PCLAF+/MAZ regulatory network. This model linked high PCLAF+ tumor risk scores with poor survival outcomes and specific immune microenvironment characteristics.

CONCLUSION

This study utilized scRNA-seq to reveal the role of the C3 PCLAF+ tumor cells subtype in HR+BC, emphasizing its association with poor prognosis and resistance to endocrine therapies. MAZ, identified as a key regulator, contributed to tumor progression, while the tumor microenvironment had a pivotal identity in immune evasion. The findings underscored the importance of overcoming drug resistance, recognizing novel treatment targets, and crafting tailored diagnosis regimens.

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 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.

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.

A novel mitochondrial-related risk model for predicting prognosis and immune checkpoint blockade therapy response in uterine corpus endometrial carcinoma

Uterine Corpus Endometrial Carcinoma (UCEC) represents a common malignant neoplasm in women, with its prognosis being intricately associated with available therapeutic interventions. In the past few decades, there has been a burgeoning interest in the role of mitochondria within the context of UCEC. Nevertheless, the development and application of prognostic models predicated on mitochondrial-related genes (MRGs) in UCEC remains in the exploratory stages. This study utilized RNA sequencing data and clinical information from the TCGA database to identify differentially expressed MRGs (DEMRGs) between UCEC and normal groups that are associated with overall survival (OS). Patients were randomly assigned to training and testing cohorts in a 1:1 ratio. In the training cohort, a risk model based on DEMRGs was developed using Lasso Cox regression analysis. Subsequently, patients in both cohorts were stratified into high-risk and low-risk groups based on their median risk scores. The prognostic performance of the model was validated through Kaplan-Meier survival analysis, ROC curves, and nomograms. Additionally, further analyses including functional enrichment, immune landscape assessment, prediction of response to ICB therapy, mutation profiling, and drug sensitivity analysis elucidated biological distinctions between the identified risk groups. We established a risk model incorporating eight MRGs. Patients classified within he high-risk group exhibited significantly poorer prognoses relative to those in the low-risk group. Functional enrichment analysis identified substantial differences in biological processes and signaling pathways between the high-risk and low-risk cohorts. Immune landscape analysis showed that patients with elevated risk scores exhibited significant immunosuppressive and immune evasion mechanisms. Conversely, low-risk patients exhibited higher expression of human leukocyte antigen (HLA) family members and immune checkpoint genes (ICGs) compared to their high-risk counterparts.Consequently, low-risk patients showed greater responsiveness to immunotherapy and potential small molecule drugs, whereas high-risk patients were more susceptible to chemotherapy. The mitochondrial-related risk model formulated in this study demonstrates efficacy in predicting both prognosis and response to immunotherapy in patients with UCEC, thereby providing a scientific basis for personalized treatment strategies. Future research endeavors should focus on further validating the clinical utility of this model and investigate the specific mechanisms of the identified MRGs in UCEC.

Influence of RNA Methylation on Cancerous Cells: A Prospective Approach for Alteration of In Vivo Cellular Composition

RNA methylation is a dynamic and ubiquitous post-transcriptional modification that plays a pivotal role in regulating gene expression in various conditions like cancer, neurological disorders, cardiovascular diseases, viral infections, metabolic disorders, and autoimmune diseases. RNA methylation manifests across diverse RNA species including messenger RNA (mRNA), ribosomal RNA (rRNA), and transfer RNA (tRNA), exerting pivotal roles in gene expression regulation and various biological phenomena. Aberrant activity of writer, eraser, and reader proteins enables dysregulated methylation landscape across diverse malignancy transcriptomes, frequently promoting cancer pathogenesis. Numerous oncogenic drivers, tumour suppressors, invasion/metastasis factors, and signalling cascade components undergo methylation changes that modulate respective mRNA stability, translation, splicing, transport, and protein-RNA interactions accordingly. Functional studies confirm methylation-dependent alterations drive proliferation, survival, motility, angiogenesis, stemness, metabolism, and therapeutic evasion programs systemically. Methyltransferase overexpression typifies certain breast, liver, gastric, and other carcinomas correlating with adverse clinical outcomes like diminished overall survival. Mapping efforts uncover nodal transcripts for targeted drug development against hyperactivated regulators including METTL3. Some erasers and readers also suitable lead candidates based on apparent synthetic lethality. Proteomic screens additionally highlight relevant methylation-sensitive effector pathways amenable to combinatorial blockade, reversing compensatory signalling mechanisms that facilitate solid tumour progression. Quantifying global methylation burdens and responsible enzymes clinically predicts patient prognosis, risk stratification for adjuvant therapy, and overall therapeutic responsiveness.

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.

Innovative strategies to optimise colorectal cancer immunotherapy through molecular mechanism insights

Background

Colorectal cancer (CRC) is a leading cause of cancer-related deaths globally. The heterogeneity of the tumor microenvironment significantly influences patient prognosis, while the diversity of tumor cells shapes its unique characteristics. A comprehensive analysis of the molecular profile of tumor cells is crucial for identifying novel molecular targets for drug sensitivity analysis and for uncovering the pathophysiological mechanisms underlying CRC.

Methods

We utilized single-cell RNA sequencing technology to analyze 13 tissue samples from 4 CRC patients, identifying key cell types within the tumor microenvironment. Intercellular communication was assessed using CellChat, and a risk score model was developed based on eight prognostic genes to enhance patient stratification for immunotherapeutic approaches. Additionally, in vitro experiments were performed on DLX2, a gene strongly associated with poor prognosis, to validate its potential role as a therapeutic target in CRC progression.

Results

Eight major cell types were identified across the tissue samples. Within the tumor cell population, seven distinct subtypes were recognized, with the C0 FXYD5+ tumor cells subtype being significantly linked to cancer progression and poor prognosis. CellChat analysis indicated extensive communication among tumor cells, fibroblasts, and immune cells, underscoring the complexity of the tumor microenvironment. The risk score model demonstrated high accuracy in predicting 1-, 3-, and 5-year survival rates in CRC patients. Enrichment analysis revealed that the C0 FXYD5+ tumor cell subtype exhibited increased energy metabolism, protein synthesis, and oxidative phosphorylation, contributing to its aggressive behavior. In vitro experiments confirmed DLX2 as a critical gene associated with poor prognosis, suggesting its viability as a target for improving drug sensitivity.

Conclusion

In summary, this study advances our understanding of CRC progression by identifying critical tumor subtypes, molecular pathways, and prognostic markers that can inform innovative strategies for predicting and enhancing drug sensitivity. These findings hold promise for optimizing immunotherapeutic approaches and developing new targeted therapies, ultimately aiming to improve patient outcomes in CRC.

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.

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.

Intratumoral microbiota as a novel prognostic indicator in bladder cancer

Microbes are important components of the tumor microenvironment and have a close relationship with tumors. However, there is still a lack of research on the intratumoral microbiota in bladder cancer and its impact on the tumor immune microenvironment. In this study, we used fluorescence in situ hybridization (FISH) and observed a substantial presence of microbiota in bladder cancer tissues, with greater abundance compared to that in normal bladder tissues. Based on the BIC database, we found that the microbiome of bladder cancer is highly diverse and its structure is significantly different from that of other tumors. To investigate the relationships among the intratumoral microbiota, tumor immunity, and prognosis in bladder cancer patients, we analyzed bladder cancer-specific differentially expressed immune- and antimicrobial-related genes from the ImmPort, TISIDB, and TCGA databases. We identified 11 hub genes and constructed a prognostic risk model. Further analysis revealed differences at the family and genus levels between distinct groups. Using LEfSe analysis, we identified six hub biomarkers and developed a novel microbial-based scoring system. The scoring system allows subgrouping of bladder cancer patients, with significant differences in prognosis, immune cell infiltration, tumor mutation burden, and immune checkpoints among different groups. Further FISH and immunofluorescence co-staining experiments initially verified that the specific distribution of microorganisms and M2 macrophages in bladder cancer may be closely related to the poor prognosis of patients. In conclusion, this study revealed the characteristics of the intratumoral microbiota in bladder cancer and identified potential prognostic targets for clinical application.

Identification of telomere-related lncRNAs and immunological analysis in ovarian cancer

Background

Ovarian cancer (OC) is a global malignancy characterized by metastatic invasiveness and recurrence. Long non-coding RNAs (lncRNAs) and Telomeres are closely connected with several cancers, but their potential as practical prognostic markers in OC is less well-defined.

Methods

Relevant mRNA and clinical data for OC were sourced from The Cancer Genome Atlas (TCGA) database. The telomere-related lncRNAs (TRLs) prognostic model was established by univariate/LASSO/multivariate regression analyses. The effectiveness of the TRLs model was evaluated and measured via the nomogram. Additionally, immune infiltration, tumor mutational load (TMB), and drug sensitivity were evaluated. We validated the expression levels of prognostic genes. Subsequently, PTPRD-AS1 knockdown was utilized to perform the CCK8 assay, colony formation assay, transwell assay, and wound healing assay of CAOV3 cells.

Results

A six-TRLs prognostic model (PTPRD-AS1, SPAG5-AS1, CHRM3-AS2, AC074286.1, FAM27E3, and AC018647.3) was established, which can effectively predict patient survival rates and was successfully validated using external datasets. According to the nomogram, the model could effectively predict prognosis. Furthermore, we detected the levels of regulatory T cells and M2 macrophages were comparatively higher in the high-risk TRLs group, but the levels of activated CD8 T cells and monocytes were the opposite. Finally, the low-risk group was more sensitive to anti-cancer drugs. The mRNA levels of PTPRD-AS1, SPAG5-AS1, FAM27E3, and AC018647.3 were significantly over-expressed in OC cell lines (SKOV3, A2780, CAOV3) in comparison to normal IOSE-80 cells. AC074286.1 were over-expressed in A2780 and CAOV3 cells and CHRM3-AS2 only in A2780 cells. PTPRD-AS1 knockdown decreased the proliferation, cloning, and migration of CAOV3 cells.

Conclusion

Our study identified potential biomarkers for the six-TRLs model related to the prognosis of OC.

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.

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.

Tumor-associated characteristics and immune dysregulation in nasopharyngeal carcinoma under the regulation of m7G-related tumor microenvironment cells

BACKGROUND

Nasopharyngeal carcinoma (NPC) is a type of malignant tumor with high morbidity. Aberrant levels of N7-methylguanosine (m7G) are closely associated with tumor progression. However, the characteristics of the tumor microenvironment (TME) in NPC associated with m7G modification remain unclear.

METHODS

A total of 68,795 single cells from single-cell RNA sequencing data derived from 11 NPC tumor samples and 3 nasopharyngeal lymphatic hyperplasia (NLH) samples were clustered using a nonnegative matrix factorization algorithm according to 61 m7G RNA modification regulators.

RESULTS

The m7G regulators were found differential expression in the TME cells of NPC, and most m7G-related immune cell clusters in NPC tissues had a higher abundance compared to non-NPC tissues. Specifically, m7G scores in the CD4+ and CD8+ T cell clusters were significantly lower in NPC than in NLH. T cell clusters differentially expressed immune co-stimulators and co-inhibitors. Macrophage clusters differentially expressed EIF4A1, and high EIF4A1 expression was associated with poor survival in patients with head and neck squamous carcinoma. EIF4A1 was upregulated in NPC tissues compared to the non-NPC tissues and mainly expressed in CD86+ macrophages. Moreover, B cell clusters exhibited tumor biological characteristics under the regulation of m7G-related genes in NPC. The fibroblast clusters interacted with the above immune cell clusters and enriched tumor biological pathways, such as FGER2 signaling pathway. Importantly, there were correlations and interactions through various ligand-receptor links among epithelial cells and m7G-related TME cell clusters.

CONCLUSION

Our study revealed tumor-associated characteristics and immune dysregulation in the NPC microenvironment under the regulation of m7G-related TME cells. These results demonstrated the underlying regulatory roles of m7G in NPC.

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.

Critical roles and clinical perspectives of RNA methylation in cancer

RNA modification, especially RNA methylation, is a critical posttranscriptional process influencing cellular functions and disease progression, accounting for over 60% of all RNA modifications. It plays a significant role in RNA metabolism, affecting RNA processing, stability, and translation, thereby modulating gene expression and cell functions essential for proliferation, survival, and metastasis. Increasing studies have revealed the disruption in RNA metabolism mediated by RNA methylation has been implicated in various aspects of cancer progression, particularly in metabolic reprogramming and immunity. This disruption of RNA methylation has profound implications for tumor growth, metastasis, and therapy response. Herein, we elucidate the fundamental characteristics of RNA methylation and their impact on RNA metabolism and gene expression. We highlight the intricate relationship between RNA methylation, cancer metabolic reprogramming, and immunity, using the well-characterized phenomenon of cancer metabolic reprogramming as a framework to discuss RNA methylation's specific roles and mechanisms in cancer progression. Furthermore, we explore the potential of targeting RNA methylation regulators as a novel approach for cancer therapy. By underscoring the complex mechanisms by which RNA methylation contributes to cancer progression, this review provides a foundation for developing new prognostic markers and therapeutic strategies aimed at modulating RNA methylation in cancer treatment.

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.

Single cell sequencing revealed the mechanism of CRYAB in glioma and its diagnostic and prognostic value

Background

We explored the characteristics of single-cell differentiation data in glioblastoma and established prognostic markers based on CRYAB to predict the prognosis of glioblastoma patients. Aberrant expression of CRYAB is associated with invasive behavior in various tumors, including glioblastoma. However, the specific role and mechanisms of CRYAB in glioblastoma are still unclear.

Methods

We assessed RNA-seq and microarray data from TCGA and GEO databases, combined with scRNA-seq data on glioma patients from GEO. Utilizing the Seurat R package, we identified distinct survival-related gene clusters in the scRNA-seq data. Prognostic pivotal genes were discovered through single-factor Cox analysis, and a prognostic model was established using LASSO and stepwise regression algorithms. Moreover, we investigated the predictive potential of these genes in the immune microenvironment and their applicability in immunotherapy. Finally, in vitro experiments confirmed the functional significance of the high-risk gene CRYAB.

Results

By analyzing the ScRNA-seq data, we identified 28 cell clusters representing seven cell types. After dimensionality reduction and clustering analysis, we obtained four subpopulations within the oligodendrocyte lineage based on their differentiation trajectory. Using CRYAB as a marker gene for the terminal-stage subpopulation, we found that its expression was associated with poor prognosis. In vitro experiments demonstrated that knocking out CRYAB in U87 and LN229 cells reduced cell viability, proliferation, and invasiveness.

Conclusion

The risk model based on CRYAB holds promise in accurately predicting glioblastoma. A comprehensive study of the specific mechanisms of CRYAB in glioblastoma would contribute to understanding its response to immunotherapy. Targeting the CRYAB gene may be beneficial for glioblastoma patients.

Identification of a novel necroptosis-related LncRNA signature for prognostic prediction and immune response in oral squamous cell carcinoma

BACKGROUND

Necroptosis is a caspase-independent regulated necrotic cell death modality that elicits strong adaptive immune responses, and has the potential to activate antitumor immunity. Long non-coding RNAs (lncRNAs) have critical effects on oral squamous cell carcinoma (OSCC), which are closely associated with the prognosis and immune regulation of OSCC patients.

OBJECTIVE

This study aimed to identify a novel necroptosis-related lncRNAs signature to predict the prognosis and immune response of OSCC patients and provide patients with anti-tumor drug selection through bioinformatics analysis and in vitro experiments.

METHODS

A series of analyses, including differential lncRNA screening, survival analysis, Cox regression analysis, ROC analysis, nomogram prediction, enrichment analysis, tumor-infiltrating immune cells, drug sensitivity analysis, and consensus cluster analysis, were performed to determine and validate the prognostic value of necroptosis-associated lncRNAs signature in OSCC. And real-time quantitative polymerase chain reaction (RT-qPCR) was used to determine the expression levels of these lncRNAs.

RESULTS

This signature including 5 lncRNAs (AC099850.3, StarD4-AS1, AC011978.1, LINC01503, CDKN2A-DT) in OSCC associated with necroptosis were established and verified by bioinformatics. Further, ROC, K-M, univariate/multivariate Cox regression, and nomogram analysis were used to evaluate the model's features for OSCC prognosis. Using multiple bioinformatics techniques, the levels of tumor-infiltrating immune cells, immune checkpoints and semi-inhibitory concentrations showed significant differences across risk subtypes. By consensus cluster analysis, there were significant differences between clusters in survival, immune checkpoint expression, clinicopathological correlation, and tumor immunity. RT-qPCR showed that AC099850.3, AC011978.1, LINC01503 were up-regulated, STARD4-AS1 and CDKN2A-DT were down-regulated in OSCC cell lines compared with human normal oral keratinoid cell line.

CONCLUSION

We established 5-NRLs markers, which is useful for assessing OSCC immune response and prognosis, recommending personalized antitumor drugs. The expression level of 5-NRLs in OSCC was identified in vitro, and the results preliminarily verified this model. And this study would generate new insights for future experimental research.

Shining a spotlight on m6A and the vital role of RNA modification in endometrial cancer: a review

RNA modifications are mostly dynamically reversible post-transcriptional modifications, of which m6A is the most prevalent in eukaryotic mRNAs. A growing number of studies indicate that RNA modification can finely tune gene expression and modulate RNA metabolic homeostasis, which in turn affects the self-renewal, proliferation, apoptosis, migration, and invasion of tumor cells. Endometrial carcinoma (EC) is the most common gynecologic tumor in developed countries. Although it can be diagnosed early in the onset and have a preferable prognosis, some cases might develop and become metastatic or recurrent, with a worse prognosis. Fortunately, immunotherapy and targeted therapy are promising methods of treating endometrial cancer patients. Gene modifications may also contribute to these treatments, as is especially the case with recent developments of new targeted therapeutic genes and diagnostic biomarkers for EC, even though current findings on the relationship between RNA modification and EC are still very limited, especially m6A. For example, what is the elaborate mechanism by which RNA modification affects EC progression? Taking m6A modification as an example, what is the conversion mode of methylation and demethylation for RNAs, and how to achieve selective recognition of specific RNA? Understanding how they cope with various stimuli as part of in vivo and in vitro biological development, disease or tumor occurrence and development, and other processes is valuable and RNA modifications provide a distinctive insight into genetic information. The roles of these processes in coping with various stimuli, biological development, disease, or tumor development in vivo and in vitro are self-evident and may become a new direction for cancer in the future. In this review, we summarize the category, characteristics, and therapeutic precis of RNA modification, m6A in particular, with the purpose of seeking the systematic regulation axis related to RNA modification to provide a better solution for the treatment of EC.

RNA N6-methyladenosine modification in female reproductive biology and pathophysiology

Gene expression and posttranscriptional regulation can be strongly influenced by epigenetic modifications. N⁶-methyladenosine, the most extensive RNA modification, has been revealed to participate in many human diseases. Recently, the role of RNA epigenetic modifications in the pathophysiological mechanism of female reproductive diseases has been intensively studied. RNA m⁶A modification is involved in oogenesis, embryonic growth, and foetal development, as well as preeclampsia, miscarriage, endometriosis and adenomyosis, polycystic ovary syndrome, premature ovarian failure, and common gynaecological tumours such as cervical cancer, endometrial cancer, and ovarian cancer. In this review, we provide a summary of the research results of m⁶A on the female reproductive biology and pathophysiology in recent years and aim to discuss future research directions and clinical applications of m⁶A-related targets. Hopefully, this review will add to our understanding of the cellular mechanisms, diagnostic biomarkers, and underlying therapeutic strategies of female reproductive system diseases. Video Abstract.