Integrating Lipidomics and Transcriptomics Reveals the Crosstalk Between Oxidative Stress and Neuroinflammation in Central Nervous System Demyelination

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.

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.

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.

Recent Advances in Metabolomics and Lipidomics Studies in Human and Animal Models of Multiple Sclerosis

Multiple sclerosis (MS) is a neurodegenerative and inflammatory disease of the central nervous system (CNS) that leads to a loss of myelin. There are three main types of MS: relapsing-remitting MS (RRMS) and primary and secondary progressive disease (PPMS, SPMS). The differentiation in the pathogenesis of these two latter courses is still unclear. The underlying mechanisms of MS are yet to be elucidated, and the treatment relies on immune-modifying agents. Recently, lipidomics and metabolomics studies using human biofluids, mainly plasma and cerebrospinal fluid (CSF), have suggested an important role of lipids and metabolites in the pathophysiology of MS. In this review, the results from studies on metabolomics and lipidomics analyses performed on biological samples of MS patients and MS-like animal models are presented and analyzed. Based on the collected findings, the biochemical pathways in human and animal cohorts involved were investigated and biological mechanisms and the potential role they have in MS are discussed. Limitations and challenges of metabolomics and lipidomics approaches are presented while concluding that metabolomics and lipidomics may provide a more holistic approach and provide biomarkers for early diagnosis of MS disease.

Regulatory T cells subgroups in the tumor microenvironment cannot be overlooked: Their involvement in prognosis and treatment strategy in melanoma

BACKGROUND

Melanoma, the most lethal form of skin cancer, presents substantial challenges despite effective surgical interventions for in situ lesions. Regulatory T cells (Tregs) wield a pivotal immunomodulatory influence within the tumor microenvironment, yet their impact on melanoma prognosis and direct molecular interactions with melanoma cells remain elusive. This investigation employs single-cell analysis to unveil the intricate nature of Tregs in human melanoma.

METHODS

Single-cell RNA and bulk sequencing data, alongside clinical information, were obtained from public repositories. Initially, GO and GSEA analyses were employed to delineate functional disparities among distinct cell subsets. Pseudotime and cell-cell interconnection analyses were conducted, followed by an endeavor to construct a prognostic model grounded in Treg-associated risk scores. This model's efficacy was demonstrated via PCA and K-M analyses, with multivariate Cox regression affirming its independent prognostic value in melanoma patients. Furthermore, immune infiltration analysis, immune checkpoint gene expression scrutiny, and drug sensitivity assessments were performed to ascertain the clinical relevance of this prognostic model.

RESULTS

Following batch effect correction, 80 025 cells partitioned into 31 clusters, encompassing B cells, plasma cells, endothelial cells, fibroblasts, melanoma cells, monocytes, macrophages, and T_NK cells. Within these, 4240 CD4+ T cells were subclassified into seven distinct types. Functional analysis underscored the immunomodulatory function of Tregs within the melanoma tumor microenvironment, elucidating disparities among Treg subpopulations. Notably, the ITGB2 signaling pathway emerged as a plausible molecular nexus linking Tregs to melanoma cells. Our prognostic signature exhibited robust predictive capacities for melanoma prognosis and potential implications in evaluating immunotherapy response.

CONCLUSION

Tregs exert a critical role in immune suppression within the melanoma tumor microenvironment, revealing a potential molecular-level association with melanoma cells. Our innovative Treg-centered signature introduces a promising prognostic marker for melanoma, holding potential for future clinical prognostic assessments.

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

BACKGROUND

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

MATERIALS AND METHODS

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

RESULT

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

CONCLUSION

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

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

Objective

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

Methods

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

Results

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

Conclusion

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

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.

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

Background

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

Materials and methods

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

Results

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

Conclusion

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

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.

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.

Combined signature of G protein-coupled receptors and tumor microenvironment provides a prognostic and therapeutic biomarker for skin cutaneous melanoma

BACKGROUND

G protein-coupled receptors (GPCRs) have been shown to have an important role in tumor development and metastasis, and abnormal expression of GPCRs is significantly associated with poor prognosis of tumor patients. In this study, we analyzed the GPCRs-related gene (GPRGs) and tumor microenvironment (TME) in skin cutaneous melanoma (SKCM) to construct a prognostic model to help SKCM patients obtain accurate clinical treatment strategies.

METHODS

SKCM expression data and clinical information were obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Differential expression analysis, LASSO algorithm, and univariate and multivariate cox regression analysis were used to screen prognosis-related genes (GPR19, GPR146, S1PR2, PTH1R, ADGRE5, CXCR3, GPR143, and OR2I1P) and multiple prognosis-good immune cells; the data set was analyzed according to above results and build up a GPR-TME classifier. The model was further subjected to immune infiltration, functional enrichment, tumor mutational load, immunotherapy prediction, and scRNA-seq data analysis. Finally, cellular experiments were conducted to validate the functionality of the key gene GPR19 in the model.

RESULTS

The findings indicate that high expression of GPRGs is associated with a poor prognosis in patients with SKCM, highlighting the significant role of GPRGs and the tumor microenvironment (TME) in SKCM development. Notably, the group characterized by low GPR expression and a high TME exhibited the most favorable prognosis and immunotherapeutic efficacy. Furthermore, cellular assays demonstrated that knockdown of GPR19 significantly reduced the proliferation, migration, and invasive capabilities of melanoma cells in A375 and A2058 cell lines.

CONCLUSION

This study provides novel insights for the prognosis evaluation and treatment of melanoma, along with the identification of a new biomarker, GPR19.

Integrative lactylation and tumor microenvironment signature as prognostic and therapeutic biomarkers in skin cutaneous melanoma

BACKGROUND

The incidence of skin cutaneous melanoma (SKCM), one of the most aggressive and lethal skin tumors, is increasing worldwide. However, for advanced SKCM, we still lack an accurate and valid way to predict its prognosis, as well as novel theories to guide the planning of treatment options for SKCM patients. Lactylation (LAC), a novel post-translational modification of histones, has been shown to promote tumor growth and inhibit the antitumor response of the tumor microenvironment (TME) in a variety of ways. We hope that this study will provide new ideas for treatment options for SKCM patients, as well as research on the molecular mechanisms of SKCM pathogenesis and development.

METHODS

At the level of the RNA sequencing set (TCGA, GTEx), we used differential expression analysis, LASSO regression analysis, and multifactor Cox regression analysis to screen for prognosis-related genes and calculate the corresponding LAC scores. The content of TME cells in the tumor tissue was calculated using the CIBERSORT algorithm, and the TME score was calculated based on its results. Finally, the LAC-TME classifier was established and further analyzed based on the two scores, including the construction of a prognostic model, analysis of clinicopathological characteristics, and correlation analysis of tumor mutation burden (TMB) and immunotherapy. Based on single-cell RNA sequencing data, this study analyzed the cellular composition in SKCM tissues and explored the role of LAC scores in intercellular communication. To validate the functionality of the pivotal gene CLPB in the model, cellular experiments were ultimately executed.

RESULTS

We screened a total of six prognosis-related genes (NDUFA10, NDUFA13, CLPB, RRM2B, HPDL, NARS2) and 7 TME cells with good prognosis. According to Kaplan-Meier survival analysis, we found that the LAClow/TMEhigh group had the highest overall survival (OS) and the LAChigh/TMElow group had the lowest OS (p value < 0.05). In further analysis of immune infiltration, tumor microenvironment (TME), functional enrichment, tumor mutational load and immunotherapy, we found that immunotherapy was more appropriate in the LAClow/TMEhigh group. Moreover, the cellular assays exhibited substantial reductions in proliferation, migration, and invasive potentials of melanoma cells in both A375 and A2058 cell lines upon CLPB knockdown.

CONCLUSIONS

The prognostic model using the combined LAC score and TME score was able to predict the prognosis of SKCM patients more consistently, and the LAC-TME classifier was able to significantly differentiate the prognosis of SKCM patients across multiple clinicopathological features. The LAC-TME classifier has an important role in the development of immunotherapy regimens for SKCM patients.

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

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

A novel signature predicts prognosis and immunotherapy in lung adenocarcinoma based on cancer-associated fibroblasts

Background

Extensive research has established the significant correlations between cancer-associated fibroblasts (CAFs) and various stages of cancer development, including initiation, angiogenesis, progression, and resistance to therapy. In this study, we aimed to investigate the characteristics of CAFs in lung adenocarcinoma (LUAD) and develop a risk signature to predict the prognosis of patients with LUAD.

Methods

We obtained single-cell RNA sequencing (scRNA-seq) and bulk RNA-seq data from the public database. The Seurat R package was used to process the scRNA-seq data and identify CAF clusters based on several biomarkers. CAF-related prognostic genes were further identified using univariate Cox regression analysis. To reduce the number of genes, Lasso regression was performed, and a risk signature was established. A novel nomogram that incorporated the risk signature and clinicopathological features was developed to predict the clinical applicability of the model. Additionally, we conducted immune landscape and immunotherapy responsiveness analyses. Finally, we performed in vitro experiments to verify the functions of EXO1 in LUAD.

Results

We identified 5 CAF clusters in LUAD using scRNA-seq data, of which 3 clusters were significantly associated with prognosis in LUAD. A total of 492 genes were found to be significantly linked to CAF clusters from 1731 DEGs and were used to construct a risk signature. Moreover, our immune landscape exploration revealed that the risk signature was significantly related to immune scores, and its ability to predict responsiveness to immunotherapy was confirmed. Furthermore, a novel nomogram incorporating the risk signature and clinicopathological features showed excellent clinical applicability. Finally, we verified the functions of EXP1 in LUAD through in vitro experiments.

Conclusions

The risk signature has proven to be an excellent predictor of LUAD prognosis, stratifying patients more appropriately and precisely predicting immunotherapy responsiveness. The comprehensive characterization of LUAD based on the CAF signature can predict the response of LUAD to immunotherapy, thus offering fresh perspectives into the management of LUAD patients. Our study ultimately confirms the role of EXP1 in facilitating the invasion and growth of tumor cells in LUAD. Nevertheless, further validation can be achieved by conducting in vivo experiments.

Ebselen alleviates white matter lesions and improves cognitive deficits by attenuating oxidative stress via Keap1/Nrf2 pathway in chronic cerebral hypoperfusion mice

Oxidative stress is crucial in cerebral white matter lesions (WMLs) induced by chronic cerebral hypoperfusion. Therefore, ameliorating oxidative damage is considered to be a beneficial strategy for the treatment of WMLs. Ebselen (EbSe), a small lipid organoselenium compound, its lipid peroxidation activity is mediated through the glutathione peroxidase-mimetic properties. This study aimed to investigate the role of EbSe in WMLs after bilateral common carotid artery stenosis (BCAS). The BCAS model can moderately reduce cerebral blood flow, and mimics white matter damage caused by chronic cerebral hypoperfusion or small vessel disease. Laser Speckle Contrast Imaging (LSCI) was used to monitor the cerebral blood flow of mice. The spatial learning and memory were tested by using the eight-arm maze. LFB staining was used to detect demyelination. The expression of MBP, GFAP and Iba1 was assayed by immunofluorescence. The demyelination was assessed by Transmission Electron Microscope (TEM). The activities of MDA, SOD and GSH-Px were detected by assay kits. The mRNA levels of SOD, GSH-Px and HO-1 was detected by realtime PCR. The activation of the Nrf2/ARE pathway and the expression of SOD, GSH-Px and HO-1was assessed by Western blot. EbSe ameliorated cognitive deficits and white matter lesions induced by bilateral common carotid artery stenosis (BCAS). The expression of GFAP and Iba1 was decreased in the corpus callosum of BCAS mice after EbSe treatment. Moreover, EbSe alleviated the level of MDA by elevating the expression and mRNA of SOD, GSH-Px and HO-1 in BCAS mice. Furthermore, EbSe promoted the dissociation of the Keap1/Nrf2 complex, resulting in the accumulation of Nrf2 in the nucleus. This study demonstrates a favorable effect of EbSe on cognitive impairment in a chronic cerebral hypoperfusion model, and the improvement of EbSe's antioxidant property is mediated by Keap1/Nrf2 pathway.

Identifying Oxidized Lipid Metabolism-Related LncRNAs as Prognostic Biomarkers of Head and Neck Squamous Cell Carcinoma

The relationship between oxidized lipid metabolism and the immunological function of cancer is well known. However, the functions and regulatory mechanisms of lncRNAs associated with oxidized lipid metabolism in head and neck squamous cell carcinoma (HNSCC) remain to be fully elucidated. In this study, we established an oxidized lipid metabolism-related lncRNA prognostic signature to assess the prognosis and immune infiltration of HNSCC patients. The HNSCC transcriptome was obtained from The Cancer Genome Atlas. The choice of the target genes with a relevance score greater than 10 was performed via a correlation analysis by GeneCards. Patients were categorized by risk score and generated with multivariate Cox regression, which was then validated and evaluated using the Kaplan–Meier analysis and time-dependent receiver operating characteristics (ROC). A nomogram was constructed by combining the risk score with the clinical data. We constructed a risk score with 24 oxidized lipid metabolism-related lncRNAs. The areas’ 1-, 2-, and 3-year OS under the ROC curve (AUC) were 0.765, 0.724, and 0.724, respectively. Furthermore, the nomogram clearly distinguished the survival probabilities of patients in high- and low-risk groups, between which substantial variations were revealed by immune infiltration analysis. The results supported the fact that oxidized lipid metabolism-related lncRNAs might predict prognoses and assist with differentiating amid differences in immune infiltration in HNSCC.

Whole-body adipose tissue multi-omic analyses in sheep reveal molecular mechanisms underlying local adaptation to extreme environments

The fat tail of sheep is an important organ that has evolved to adapt to extreme environments. However, the genetic mechanisms underlying the fat tail phenotype remain poorly understood. Here, we characterize transcriptome and lipidome profiles and morphological changes in 250 adipose tissues from two thin-tailed and three fat-tailed sheep populations in summer and winter. We implement whole-genome selective sweep tests to identify genetic variants related to fat-tails. We identify a set of functional genes that show differential expression in the tail fat of fat-tailed and thin-tailed sheep in summer and winter. These genes are significantly enriched in pathways, such as lipid metabolism, extracellular matrix (ECM) remodeling, molecular transport, and inflammatory response. In contrast to thin-tailed sheep, tail fat from fat-tailed sheep show slighter changes in adipocyte size, ECM remodeling, and lipid metabolism, and had less inflammation in response to seasonal changes, indicating improved homeostasis. Whole-genome selective sweep tests identify genes involved in preadipocyte commitment (e.g., BMP2, PDGFD) and terminal adipogenic differentiation (e.g., VEGFA), which could contribute to enhanced adipocyte hyperplasia. Altogether, we establish a model of regulatory networks regulating adipose homeostasis in sheep tails. These findings improve our understanding of how adipose homeostasis is maintained, in response to extreme environments in animals.

Multi-omics analysis revealed the role of CYP1A2 in the induction of mechanical allodynia in type 1 diabetes

Background: Mechanical allodynia (MA) is one of the leading clinical symptoms of painful diabetic peripheral neuropathy (PDPN), which is a primary reason for non-traumatic amputations, foot ulceration, and gait abnormalities in patients with diabetes. However, the pathogenic mechanisms of MA have not yet been fully elucidated, and there is no effective treatment. This study aims to study the potential pathogenetic mechanisms of MA and to provide targets for the therapy of MA. Methods: A single intraperitoneal injection of streptozotocin induced type 1 diabetes in rat models. Subsequently, rats were divided into the control group, the diabetic group without MA, and the diabetic group with MA based on weekly behavioral assays. The differentially expressed lipids in the sciatic nerve of each group were detected using untargeted lipidomics, and the differentially expressed genes in the sciatic nerve of each group were detected by transcriptomics. The pathogenesis of MA was predicted using integrated analysis and validated by immunofluorescence staining and transmission electron microscopy. Results: Untargeted lipidomics revealed the accumulation of a more severe lipid in MA rats. Transcriptomics results suggested that differentially expressed genes in MA rats were primarily related to lipid droplets and myelin sheath. Integrated analysis results indicated that the downregulation of Cytochrome P450 1A2 (CYP1A2) expression was closely linked to lipid metabolism disorders. Immunofluorescence staining demonstrated that down-regulation of CYP1A2 expression occurred in MA rats. Transmission electron microscopy results showed that more severe lipid droplet accumulation and myelin sheath degeneration occurred in MA rats. Conclusion: Our findings imply that the downregulation of CYP1A2 expression leads to disorders of lipid metabolism and further leads to lipid droplet accumulation and myelin sheath degeneration, which might ultimately lead to the development of MA. Therefore, our study contributes to promoting the understanding of the molecular mechanisms of MA and providing potential targets for the clinical treatment of MA.