The construction and experimental verification of a 6-LncRNA model based on Lactic acid metabolism in the tumor microenvironment of Wilms tumor.

BACKGROUND: Lactic acid (LA) can promote the malignant progression of tumors through the crosstalk with the tumor microenvironment (TME). However, the function of long non-coding RNAs (lncRNAs) related to LA metabolism in Wilms tumor (WT) remains unclear. METHODS: Gene expression data and clinical data of WT patients were collected from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO). Through the ESTIMATE algorithm and Pearson correlation analysis, lncRNAs related to tumor immunity and LA metabolism were screened. Subsequently, Cox regression analysis and Lasso Cox regression analysis were used to construct a model. Furthermore, candidate genes were identified and a competitive endogenous RNA (ceRNA) network was conducted to explore the specific mechanism of characteristic genes. Finally, based on the strong clinical relevance of UNC5B-AS1, its expression and function were experimentally verified. RESULTS: The immune score and stromal score were found to be closely related to the prognosis of WT. Eventually, a prognostic model (TME-LA-LM) consisting of 6 lncRNAs was successfully identified. The model demonstrated favorable predictive ability and accuracy, with significant variation in immune infiltration and drug susceptibility observed between risk groups. Additionally, the study revealed the involvement of 2 candidate genes and 5 microRNAs (miRNAs) in the tumor's development. Notably, UNC5B-AS1 was highly expressed and found to promote the proliferation and migration of tumor cells. CONCLUSION: This study, for the first time, elucidated the prognostic signatures of WT using lncRNAs related to TME and LA metabolism. The fundings of this research offer valuable insights for future studies on immunotherapy, personalized chemotherapy and mechanism research.

Single-cell and spatial omics unravel the spatiotemporal biology of tumour border invasion and haematogenous metastasis.

Solid tumours exhibit a well-defined architecture, comprising a differentiated core and a dynamic border that interfaces with the surrounding tissue. This border, characterised by distinct cellular morphology and molecular composition, serves as a critical determinant of the tumour's invasive behaviour. Notably, the invasive border of the primary tumour represents the principal site for intravasation of metastatic cells. These cells, known as circulating tumour cells (CTCs), function as 'seeds' for distant dissemination and display remarkable heterogeneity. Advancements in spatial sequencing technology are progressively unveiling the spatial biological features of tumours. However, systematic investigations specifically targeting the characteristics of the tumour border remain scarce. In this comprehensive review, we illuminate key biological insights along the tumour body-border-haematogenous metastasis axis over the past five years. We delineate the distinctive landscape of tumour invasion boundaries and delve into the intricate heterogeneity and phenotype of CTCs, which orchestrate haematogenous metastasis. These insights have the potential to explain the basis of tumour invasion and distant metastasis, offering new perspectives for the development of more complex and precise clinical interventions and treatments.

Single-cell and spatial sequencing identifies senescent and germinal tumor cells in adamantinomatous craniopharyngiomas.

Adamantinomatous craniopharyngioma (ACP) is a clinically aggressive tumor without effective treatment method. Previous studies proposed a paracrine tumorigenesis model, in which oncogenic ß-catenin induces senescence in pituitary stem cells and the senescent cells lead the formation of paracrine tumors through secretion of pro-tumorigenic factors. However, there lacks characterization on senescent cells in ACPs. Here, we profiled 12 ACPs with single-cell RNA and TCR-sequencing to elucidate the cellular atlas in ACPs and 3 of them were also subject to spatial sequencing to localize different subpopulations of the tumor cells. In total, we obtained the transcriptome profiles of 70,682 cells. Tumor cells, which were unambiguously identified through the cellular mutation status of the driver CTNNB1 mutations, were clustered into 6 subsets. The whorl-like cluster (WC) cells show distinct molecular features from the other tumor cells and the palisading epithelium (PE) cells consists of a proliferating subset. Other than typical PE and WC, we identified two novel subpopulations of the tumor cells. In one subpopulation, the cells express a high level of cytokines, e.g., FDCSP and S100A8/A9, and are enriched with the senescence-associated secretory phenotype (SASP) factors. Hematoxylin and eosin staining reveals that these SASP cells lack an ordered structures and their nuclei are elongated. In the other subpopulation, the cell sizes are small and they are tightly packed together with an unusual high density expressing a high level of mitochondrial genes (median 10.9%). These cells are the origin of the tumor developmental trajectories revealed by RNA velocity and pseudo-time analysis. Single-cell RNA and TCR analysis reveals that some ACPs are infiltrated with clonally expanded cytotoxic T cells. We propose a hypothesis that WC and PE are formed via different negative regulation mechanisms of the overactivated WNT/ß-catenin signaling which provides a new understanding on the tumorigenesis of ACPs. The study lays a foundation for future studies on targeting senescent cells in ACPs with senolytic compounds or other therapeutic agents.

Potential regulation and prognostic model of colorectal cancer with extracellular matrix genes.

Background: The tumor microenvironment (TME) of colorectal cancer (CRC) mainly comprises immune cells, stromal cells, tumor cells, as well as the extracellular matrix (ECM), which holds a pivotal position. The ECM affects cancer progression, but its regulatory roles and predictive potential in CRC are not fully understood. Methods: We analyzed transcriptomes from CRC tumors and paired normal tissues to study ECM features. Up-regulated ECM components were examined through functional enrichment analysis, and single-cell sequencing identified cell types producing collagen, regulators, and secreted factors. Transcription factor analysis and cell-cell interaction studies were conducted to identify potential regulators of ECM changes. Additionally, a prognostic model was developed using TCGA-CRC cohort data, focusing on up-regulated core ECM components. Results: Bulk RNA-seq analysis revealed a unique ECM pattern in tumors, with ECM abundance and composition significantly related to patient survival. Up-regulated ECM components were linked to various cancer-related pathways. Fibroblasts and non-fibroblasts interactions were crucial in forming the TME. Key potential regulators identified included ZNF469, PRRX2, TWIST1, and AEBP1. A prognostic model based on five ECM genes (THBS3, LAMB3, ESM1, SPRX, COL9A3) demonstrated strong associations with immune suppression and tumor angiogenesis. Conclusions: The ECM components were involved in various cell-cell interactions and correlated with tumor development and poor survival outcomes. The ECM prognostic model components could be potential targets for novel therapeutic interventions in colorectal cancer.

Integrated analysis of single-cell RNA-seq and bulk RNA-seq reveals MMP mediated expression patterns by distinct tumor microenvironment immune profiles in cervical cancer.

Background: Few studies have analyzed the effect of matrix metalloproteinase (MMP) expression patterns on the tumor microenvironment (TME) during development of cervical cancer (CC). Methods: We elucidated the landscape and score of MMP expression in CC using single-cell RNA sequencing (scRNA-seq) and RNA sequencing datasets. Further, we aimed the MMPscore to probe the infiltration of immune cells. Further, MMP expression was measured by quantitative Real-Time Polymerase Chain Reaction (qRT-PCR). Results: We found MMPs were cell-type specific expressed in diverse types of CC cells, regulating the relative pathways of CC progression. Two distinct MMP expression patterns that associated infiltrated tumor microenvironment (TME) were identified. We discovered MMP expression patterns can predict the stage of tumor, subtype, stromal activity in the TME, genetic variation, and patient outcome. Patients with high MMPscore benefited from significantly better treatment and clinical outcomes. Conclusion: These results indicate high MMPscore in diverse cell types may regulate immune response and improve the survival of patients with CC, which assist in developing more effective immunization strategies.

Leveraging single-cell RNA-seq for uncovering naïve B cells associated with better prognosis of hepatocellular carcinoma.

Hepatocellular carcinoma (HCC) is a typical highly heterogeneous solid tumor with high morbidity and mortality worldwide, especially in China; however, the immune microenvironment of HCC has not been clarified so far. Here, we employed single-cell RNA sequencing (scRNA-seq) on diethylnitrosamine (DEN)-induced mouse HCC model to dissect the immune cell dynamics during tumorigenesis. Our findings reveal distinct immune profiles in both precancerous and cancerous lesions, indicating early tumor-associated immunological alterations. Notably, specific T and B cell subpopulations are preferentially enriched in the HCC tumor microenvironment (TME). Furthermore, we identified a subpopulation of naïve B cells with high CD83 expression, correlating with improved prognosis in human HCC. These signature genes were validated in The Cancer Genome Atlas HCC RNA-seq dataset. Moreover, cell interaction analysis revealed that subpopulations of B cells in both mouse and human samples are activated and may potentially contribute to oncogenic processes. In summary, our study provides insights into the dynamic immune microenvironment and cellular networks in HCC pathogenesis, with a specific emphasis on naïve B cells. These findings emphasize the significance of targeting TME in HCC patients to prevent HCC pathological progression, which may give a new perspective on the therapeutics for HCC.

Dynamic changes in pyroptosis following spinal cord injury and the identification of crucial molecular signatures through machine learning and single-cell sequencing.

The pathological cascade of spinal cord injury (SCI) is highly intricate. The onset of neuroinflammation can exacerbate the extent of damage. Pyroptosis is a form of inflammation-linked programmed cell death (PCD), the inhibition of pyroptosis can partially mitigate neuroinflammation. It is imperative to delineate the principal cell types susceptible to pyroptosis and concomitantly identify key genes associated with this process. We initially defined the pyroptosis-related genes (PRGs) and analyzed their expression at different time points post SCI. The results demonstrate a substantial upregulation of differentially expressed genes (DEGs) related to pyroptosis on the 7 days post-injury (dpi), these DEGs in the 7 dpi are closely related to the inflammatory response. Subsequently, immune infiltration analysis revealed a predominant presence of inflammatory microglia. Through correlation analysis, we postulated that pyroptosis primarily manifested within the inflammatory microglia. Employing machine learning algorithms, we identified four pyroptosis-related molecular signatures, which were experimentally validated using BV2 cells and spinal cord tissue samples. The robustness of the identified molecular signatures was further confirmed through single-cell sequencing data analysis. Overall, our study elucidates the temporal dynamics of pyroptosis and identifies key molecular signatures following SCI. These findings can provide novel evidence for therapeutic interventions in SCI.

Macrophage manufacturing and engineering with 5'-Cap1 and N1-methylpseudouridine-modified mRNA.

Macrophage-based cell therapeutics is an emerging modality to treat cancer and repair tissue damage. A reproducible manufacturing and engineering process is central to fulfilling their therapeutic potential. Here, we establish a robust macrophage-manufacturing platform (Mo-Mac) and demonstrate that macrophage functionality can be enhanced by N1-methylpseudouridine (m1Ψ)-modified mRNA. Using single-cell transcriptomic analysis as an unbiased approach, we found that >90% cells in the final product were macrophages while the rest primarily comprised T cells, B cells, natural killer cells, promyelocytes, promonocytes, and hematopoietic stem cells. This analysis also guided the development of flow-cytometry strategies to assess cell compositions in the manufactured product to meet requirements by the National Medical Products Administration. To modulate macrophage functionality, as an illustrative example we examined whether the engulfment capability of macrophages could be enhanced by mRNA technology. We found that efferocytosis was increased in vitro when macrophages were electroporated with m1Ψ-modified mRNA encoding CD300LF (CD300LF-mRNA-macrophage). Consistently, in a mouse model of acute liver failure, CD300LF-mRNA-macrophages facilitated organ recovery from acetaminophen-induced hepatotoxicity. These results demonstrate a GMP-compliant macrophage-manufacturing process and indicate that macrophages can be engineered by versatile mRNA technology to achieve therapeutic goals.

ScRNAs reveals high-frequency rTMS-induced pericyte differentiation: Potential implications for vascular regeneration and blood-brain barrier stability in stroke.

Stroke is a major cause of adult disability worldwide, often involving disruption of the blood-brain barrier (BBB). Repairing the BBB is crucial for stroke recovery, and pericytes, essential components of the BBB, are potential intervention targets. Repetitive transcranial magnetic stimulation (rTMS) has been proposed as a treatment for functional impairments after stroke, with potential effects on BBB integrity. However, the underlying mechanisms remain unclear. In this study using a transient middle cerebral artery occlusion (tMCAO) rat model, we investigated the impact of rTMS on post-stroke BBB. Through single-cell sequencing (ScRNAs), we observed developmental relationships among pericytes, endothelial cells, and vascular smooth muscle cells, suggesting the differentiation potential of pericytes. A distinct subcluster of pericytes emerged as a potential therapeutic target for stroke. Additionally, our results revealed enhanced cellular communication among these cell types, enriching signaling pathways such as IGF, TNF, NOTCH, and ICAM. Analysis of differentially expressed genes highlighted processes related to stress, differentiation, and development. Notably, rTMS intervention upregulated Reck in vascular smooth muscle cells, implicating its role in the classical Wnt signaling pathway. Overall, our bioinformatics findings suggest that rTMS may modulate BBB permeability and promote vascular regeneration following stroke. This might happen through 20 Hz rTMS promoting pericyte differentiation into vascular smooth muscle cells, upregulating Reck, then activating the classical Wnt signaling pathway, and facilitating vascular regeneration and BBB stability.

Unraveling the role of bisphenol A in osteosarcoma biology: insights into prognosis and immune microenvironment modulation.

BACKGROUND: Bisphenol A (BPA) is a common environmental pollutant, and its specific mechanisms in cancer development and its impact on the tumor immune microenvironment are not yet fully understood. METHODS: Transcriptome data from osteosarcoma (OS) patients were downloaded from the Therapeutically Applicable Research to Generate Effective Treatments (TARGET) database. BPA-related genes were identified through the Comparative Toxicogenomics Database (CTD), yielding 177 genes. Differentially expressed genes were analyzed using the GSE162454 dataset from the Tumor Immune Single Cell Hub 2 (TISCH2). We constructed the prognostic model using univariate Cox regression and LASSO analysis. The model was validated using the GSE16091 dataset. GO, KEGG, and GSEA analyses were performed to investigate the mechanisms of BPA-related genes. RESULTS: A total of 15 BPA-related genes were identified as differentially expressed in OS. Univariate Cox regression and LASSO analysis identified four key prognostic genes (FOLR1, MYC, ESRRA, VEGFA). The prognostic model exhibited strong predictive performance with area under the curve (AUC) values of 0.89, 0.6, and 0.79 for predicting 1-, 2-, and 3-year survival, respectively. External validation using the GSE16091 dataset confirmed the model's high accuracy with AUC values exceeding 0.88. Our results indicated that the prognosis of the high-risk population is generally poorer, which may be associated with alterations in the tumor immune microenvironment. In the high-risk group, immune cells showed predominantly low expression levels, while immune checkpoint genes were significantly overexpressed, along with markedly elevated tumor purity. These findings revealed a correlation between upregulation of BPA-related genes and formation of an immunosuppressive microenvironment, leading to unfavorable patient outcomes. CONCLUSION: Our study highlighted the significant association of BPA with OS biology, particularly in its potential role in modulating the tumor immune microenvironment. We offered a fresh insight into the influence of BPA on cancer development, thus providing valuable insights for future clinical interventions and treatment strategies.

Identification of feature genes and molecular mechanisms involved in cell communication in uveal melanoma through analysis of single­cell sequencing data.

Uveal melanoma (UM) is a highly metastatic cancer with resistance to immunotherapy. The present study aimed to identify novel feature genes and molecular mechanisms in UM through analysis of single-cell sequencing data. For this purpose, data were downloaded from The Cancer Genome Atlas and National Center for Biotechnology Information Gene Expression Omnibus public databases. The statistical analysis function of the CellPhoneDB software package was used to analyze the ligand-receptor relationships of the feature genes. The Metascape database was used to perform the functional annotation of notable gene sets. The randomForestSRC package and random survival forest algorithm were applied to screen feature genes. The CIBERSORT algorithm was used to analyze the RNA-sequencing data and infer the relative proportions of the 22 immune-infiltrating cell types. In vitro, small interfering RNAs were used to knockdown the expression of target genes in C918 cells. The migration capability and viability of these cells were then assessed by gap closure and Cell Counting Kit-8 assays. In total, 13 single-cell sample subtypes were clustered by t-distributed Stochastic Neighbor Embedding and annotated by the R package, SingleR, into 7 cell categories: Tissue stem cells, epithelial cells, fibroblasts, macrophages, natural killer cells, neurons and endothelial cells. The interactions in NK cells|Endothelial cells, Neurons|Endothelial cells, CD74_APP, and SPP1_PTGER4 were more significant than those in the other subsets. T-Box transcription factor 2, tropomyosin 4, plexin D1 (PLXND1), G protein subunit α I2 (GNAI2) and SEC14-like lipid binding 1 were identified as the feature genes in UM. These marker genes were found to be significantly enriched in pathways such as vasculature development, focal adhesion and cell adhesion molecule binding. Significant correlations were observed between key genes and immune cells as well as immune factors. Relationships were also observed between the expression levels of the key genes and multiple disease-related genes. Knockdown of PLXND1 and GNAI2 expression led to significantly lower viability and gap closure rates of C918 cells. Therefore, the results of the present study uncovered cell communication between endothelial cells and other cell types, identified innovative key genes and provided potential targets of gene therapy in UM.

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.

Epithelial cell-related prognostic risk model in breast cancer based on single-cell and bulk RNA sequencing.

Objective: This study aims to construct an epithelial cell-related prognostic risk model for breast cancer (BRCA) and explore its significance. Methods: GSE42568, GSE10780, GSE245601, and TCGA-BRCA datasets were sourced from public databases. Epithelial cell-related differentially expressed genes were identified using single-cell data analysis. Venn diagrams determined the intersecting genes between epithelial cell-related and BRCA-related genes. Batch Kaplan-Meier (K-M) survival analysis identified core intersecting genes for BRCA overall survival. Consensus clustering, enrichment, LASSO, and COX regression analyses were performed on the core intersecting genes, and then a prognostic risk model was constructed. The diagnostic and prognostic effectiveness of the risk model was subsequently evaluated and immune infiltration analysis was conducted. Finally, qRT-PCR was used to verify the expression of genes in the risk model. Results: There were 374 intersecting genes between epithelial cell-related and BRCA-related genes, among which 51 core intersecting genes were associated with BRCA prognosis. Consensus clustering categorized TCGA-BRCA into C1 and C2, with shared regulation of the estrogen signaling pathway. Three genes (DIRC3, SLC6A2, TUBA3D) were independent predictors of BRCA prognosis, forming the basis for a risk model. Except for exhibiting satisfactory diagnostic efficacy, the risk score elevation correlated with poor prognosis, elevated matrix, immune, and ESTIMATE scores, and negative correlation with microsatellite instability. The in vitro results confirmed the differential expression levels of DIRC3, SLC6A2, and TUBA3D. Conclusion: The prognostic risk model associated with epithelial cells demonstrates effective diagnostic performance in BRCA, serving as an independent prognostic factor for BRCA patients. Additionally, it exhibits a correlation with immune scores.

Exploiting branched-chain amino acid metabolism and NOTCH3 expression to predict and target colorectal cancer progression.

Background: The interplay between colon adenocarcinoma (COAD) and branched-chain amino acid (BCAA) metabolism is not fully understood, presenting a crucial area for investigation. Methods: We developed a prognostic model based on BCAA metabolism using the least absolute shrinkage and selection operator (LASSO) regression algorithm. We employed qRT-PCR and Western blot analyses to examine NOTCH3 expression in COAD tissues versus adjacent non-cancerous tissues and various cell lines. We also investigated the impact of NOTCH3 on COAD cell proliferation, invasion, and migration through in vitro and in vivo experiments. Results: Our BCAA metabolism-related signature (BRS) distinguished between different immune features, tumor mutation burdens, responses to immunotherapy, and drug sensitivity among COAD patients. NOTCH3 was found to be overexpressed in COAD, promoting tumor growth as verified through various assays. The model effectively predicted COAD prognosis and patient responses to treatments, underscoring the potential of BCAA pathways as therapeutic targets. Conclusion: The BRS is instrumental in predicting the prognosis and therapeutic response in COAD, with NOTCH3 playing a significant role in the proliferation, invasion and migration of COAD. These findings suggest that targeting BCAA metabolism and NOTCH3 could advance COAD treatment strategies.

Loss-of-function variants in RNA binding motif protein X-linked induce neuronal defects contributing to amyotrophic lateral sclerosis pathogenesis.

Despite being one of the most prevalent RNA modifications, the role of N6-methyladenosine (m6A) in amyotrophic lateral sclerosis (ALS) remains ambiguous. In this investigation, we explore the contribution of genetic defects of m6A-related genes to ALS pathogenesis. We scrutinized the mutation landscape of m6A genes through a comprehensive analysis of whole-exome sequencing cohorts, encompassing 508 ALS patients and 1660 population-matched controls. Our findings reveal a noteworthy enrichment of RNA binding motif protein X-linked (RBMX) variants among ALS patients, with a significant correlation between pathogenic m6A variants and adverse clinical outcomes. Furthermore, Rbmx knockdown in NSC-34 cells overexpressing mutant TDP43Q331K results in cell death mediated by an augmented p53 response. Similarly, RBMX knockdown in ALS motor neurons derived from induced pluripotent stem cells (iPSCs) manifests morphological defects and activation of the p53 pathway. Transcriptional analysis using publicly available single-cell sequencing data from the primary motor cortex indicates that RBMX-regulated genes selectively influence excitatory neurons and exhibit enrichment in ALS-implicated pathways. Through integrated analyses, our study underscores the emerging roles played by RBMX in ALS, suggesting a potential nexus between the disease and dysregulated m6A-mediated mRNA metabolism.

Application and research progress of single cell sequencing technology in leukemia.

Leukemia is a malignant tumor with high heterogeneity and a complex evolutionary process. It is difficult to resolve the heterogeneity and clonal evolution of leukemia cells by applying traditional bulk sequencing techniques, thus preventing a deep understanding of the mechanisms of leukemia development and the identification of potential therapeutic targets. However, with the development and application of single-cell sequencing technology, it is now possible to investigate the gene expression profile, mutations, and epigenetic features of leukemia at the single-cell level, thus providing a new perspective for leukemia research. In this article, we review the recent applications and advances of single-cell sequencing technology in leukemia research, discuss its potential for enhancing our understanding of the mechanisms of leukemia development, discovering therapeutic targets and personalized treatment, and provide reference guidelines for the significance of this technology in clinical research.

Proteogenomics offers a novel avenue in neoantigen identification for cancer immunotherapy.

Cancer neoantigens are tumor-specific non-synonymous mutant peptides that activate the immune system to produce an anti-tumor response. Personalized cancer vaccines based on neoantigens are currently one of the most promising therapeutic approaches for cancer treatment. By utilizing the unique mutations within each patient's tumor, these vaccines aim to elicit a strong and specific immune response against cancer cells. However, the identification of neoantigens remains challenging due to the low accuracy of current prediction tools and the high false-positive rate of candidate neoantigens. Since the concept of "proteogenomics" emerged in 2004, it has evolved rapidly with the increased sequencing depth of next-generation sequencing technologies and the maturation of mass spectrometry-based proteomics technologies to become a more comprehensive approach to neoantigen identification, allowing the discovery of high-confidence candidate neoantigens. In this review, we summarize the reason why cancer neoantigens have become attractive targets for immunotherapy, the mechanism of cancer vaccines and the advances in cancer immunotherapy. Considerations relevant to the application emerging of proteogenomics technologies for neoantigen identification and challenges in this field are described.

Multiomics integrated analysis and experimental validation identify TLR4 and ALOX5 as oxidative stress-related biomarkers in intracranial aneurysms.

BACKGROUND: Intracranial aneurysm (IA) is a severe cerebrovascular disease, and effective gene therapy and drug interventions for its treatment are still lacking. Oxidative stress (OS) is closely associated with the IA, but the key regulatory genes involved are still unclear. Through multiomics analysis and experimental validation, we identified two diagnostic markers for IA associated with OS. METHODS: In this study, we first analyzed the IA dataset GSE75436 and conducted a joint analysis of oxidative stress-related genes (ORGs). Differential analysis, functional enrichment analysis, immune infiltration, WGCNA, PPI, LASSO, and other methods were used to identify IA diagnostic markers related to OS. Next, the functions of TLR4 and ALOX5 expression in IA and their potential targeted therapeutic drugs were analyzed. We also performed single-cell sequencing of patient IA and control (superficial temporal artery, STA) tissues. 23,342 cells were captured from 2 IA and 3 STA samples obtained from our center. Cell clustering and annotation were conducted using R software to observe the distribution of TLR4 and ALOX5 expression in IAs. Finally, the expression of TLR4 and ALOX5 were validated in IA patients and in an elastase-induced mouse IA model using experiments such as WB and immunofluorescence. RESULTS: Through bioinformatics analysis, we identified 16 key ORGs associated with IA pathogenesis. Further screening revealed that ALOX5 and TLR4 were highly expressed to activate a series of inflammatory responses and reduce the production of myocytes. Methotrexate (MTX) may be a potential targeted drug. Single-cell analysis revealed a notable increase in immune cells in the IA group, with ALOX5 and TLR4 primarily localized to monocytes/macrophages. Validation through patient samples and mouse models confirmed high expression of ALOX5 and TLR4 in IAs. CONCLUSIONS: Bioinformatics analysis indicated that ALOX5 and TLR4 are the most significant ORGs associated with the pathogenesis of IA. Single-cell sequencing and experiments revealed that the high expression of ALOX5 and TLR4 are closely related to IA. These two genes are promising new targets for IA therapy.

Identification of Disulfidptosis-Related Genes in Ischemic Stroke by Combining Single-Cell Sequencing, Machine Learning Algorithms, and In Vitro Experiments.

BACKGROUND: Ischemic stroke (IS) is a severe neurological disorder with a pathogenesis that remains incompletely understood. Recently, a novel form of cell death known as disulfidptosis has garnered significant attention in the field of ischemic stroke research. This study aims to investigate the mechanistic roles of disulfidptosis-related genes (DRGs) in the context of IS and to examine their correlation with immunopathological features. METHODS: To enhance our understanding of the mechanistic underpinnings of disulfidptosis in IS, we initially retrieved the expression profile of peripheral blood from human IS patients from the GEO database. We then utilized a suite of machine learning algorithms, including LASSO, random forest, and SVM-RFE, to identify and validate pivotal genes. Furthermore, we developed a predictive nomogram model, integrating multifactorial logistic regression analysis and calibration curves, to evaluate the risk of IS. For the analysis of single-cell sequencing data, we employed a range of analytical tools, such as "Monocle" and "CellChat," to assess the status of immune cell infiltration and to characterize intercellular communication networks. Additionally, we utilized an oxygen-glucose deprivation (OGD) model to investigate the effects of SLC7A11 overexpression on microglial polarization. RESULTS: This study successfully identified key genes associated with disulfidptosis and developed a reliable nomogram model using machine learning algorithms to predict the risk of ischemic stroke. Examination of single-cell sequencing data showed a robust correlation between disulfidptosis levels and the infiltration of immune cells. Furthermore, "CellChat" analysis elucidated the intricate characteristics of intercellular communication networks. Notably, the TNF signaling pathway was found to be intimately linked with the disulfidptosis signature in ischemic stroke. In an intriguing finding, the OGD model demonstrated that SLC7A11 expression suppresses M1 polarization while promoting M2 polarization in microglia. CONCLUSION: The significance of our findings lies in their potential to shed light on the pathogenesis of ischemic stroke, particularly by underscoring the pivotal role of disulfidptosis-related genes (DRGs). These insights could pave the way for novel therapeutic strategies targeting DRGs to mitigate the impact of ischemic stroke.

Multi-omics and Single Cell Sequencing Analyses Reveal Associations of Mitophagy-Related Genes Predicting Clinical Prognosis and Immune Infiltration Characteristics in Osteosarcoma.

Despite recent advances in clinical treatments, identifying high-risk osteosarcoma (OS) patients remains an unresolved clinical challenge. Mitophagy, a specialized form of cellular autophagy, selectively reduces the number of mitochondria or repairs their abnormal functions in response to external stress, thereby ensuring mitochondrial quality and maintaining mitochondrial function. Mitophagy plays a crucial role in cancer development, including processes such as mitochondrial repair, homeostasis maintenance, and tumor metabolism. However, its impact on OS has not yet been reported. In this study, we collected 58 mitophagy-related genes (MPRGs) from the TARGET and GEO databases and bioinformatically screened for those associated with OS prognosis. By LASSO-multivariable Cox regression algorithm, we subsequently developed a novel scoring system, the MPRG score, and validated its significance in predicting OS prognosis. Immune landscape analysis showed patients in the low MPRG group had a higher immune infiltration level than those in the high MPRG group. Drug sensitivity differences highlighted the potential need for alternative therapeutic strategies based on MPRG scoring system. The distribution characteristics of the MPRG signature in different cell subtypes of OS were explored by single-cell sequencing analyses. In vitro experiments further confirmed the abnormal expression of screened targets in OS. Our findings highlight the role of mitophagy in OS and its potential as a therapeutic target.