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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.

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INTRODUCTION: To explore the cytological characteristics of tetralogy of Fallot (TOF), we collected samples and investigated the differences in the cytological classification between normal fetal hearts and fetal hearts with congenital defects. We then performed single-cell sequencing analysis to search for possible differential genes of disease markers. METHODS: Here, the right ventricles of a heart sample with TOF and a healthy human fetal heart sample were analyzed through single-cell sequencing. Data quality control filtering, comparison, quantification, and identification of recovered cells on the raw data were performed using Cell Ranger, thereby ultimately obtaining gene expression matrices for each cell. Subsequently, Seurat was used for cell filtration, standardization, cell subgroup classification, differential expression gene analysis of each subgroup, and marker gene screening. RESULTS: Bioinformatic analysis identified 9,979 and 15,224 cells from the healthy and diseased samples, respectively, with an average read depth of 25,000/cell. The cardiomyocyte cell populations, derived from the abnormal samples identified through the first-level graph-based analysis, were separated into six distinct cell clusters. CONCLUSION: Our study provides some information on TOF in a fetus, which can offer a new reference for the early detection and treatment of TOF by comparing defective heart cells with normal heart cells.

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BACKGROUND: The complexity of the immune microenvironment has an impact on the treatment of colorectal cancer (CRC), one of the most prevalent malignancies worldwide. In this study, multi-omics and single-cell sequencing techniques were used to investigate the mechanism of action of circulating and infiltrating B cells in CRC. By revealing the heterogeneity and functional differences of B cells in cancer immunity, we aim to deepen our understanding of immune regulation and provide a scientific basis for the development of more effective cancer treatment strategies. AIM: To explore the role of circulating and infiltrating B cell subsets in the immune microenvironment of CRC, explore the potential driving mechanism of B cell development, analyze the interaction between B cells and other immune cells in the immune microenvironment and the functions of communication molecules, and search for possible regulatory pathways to promote the anti-tumor effects of B cells. METHODS: A total of 69 paracancer (normal), tumor and peripheral blood samples were collected from 23 patients with CRC from The Cancer Genome Atlas database (https://portal.gdc.cancer.gov/). After the immune cells were sorted by multicolor flow cytometry, the single cell transcriptome and B cell receptor group library were sequenced using the 10X Genomics platform, and the data were analyzed using bioinformatics tools such as Seurat. The differences in the number and function of B cell infiltration between tumor and normal tissue, the interaction between B cell subsets and T cells and myeloid cell subsets, and the transcription factor regulatory network of B cell subsets were explored and analyzed. RESULTS: Compared with normal tissue, the infiltrating number of CD20+B cell subsets in tumor tissue increased significantly. Among them, germinal center B cells (GCB) played the most prominent role, with positive clone expansion and heavy chain mutation level increasing, and the trend of differentiation into memory B cells increased. However, the number of plasma cells in the tumor microenvironment decreased significantly, and the plasma cells secreting IgA antibodies decreased most obviously. In addition, compared with the immune microenvironment of normal tissues, GCB cells in tumor tissues became more closely connected with other immune cells such as T cells, and communication molecules that positively regulate immune function were significantly enriched. CONCLUSION: The role of GCB in CRC tumor microenvironment is greatly enhanced, and its affinity to tumor antigen is enhanced by its significantly increased heavy chain mutation level. Meanwhile, GCB has enhanced its association with immune cells in the microenvironment, which plays a positive anti-tumor effect.

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BACKGROUND: Diabetic foot ulcers (DFU) is the most serious complication of diabetes mellitus, which has become a global health problem due to its high morbidity and disability rates and the poor efficacy of conventional treatments. Thus, it is urgent to identify novel molecular targets to improve the prognosis and reduce disability rate in DFU patients. RESULTS: In the present study, bulk RNA-seq and scRNA-seq associated with DFU were downloaded from the GEO database. We identified 1393 DFU-related DEGs by differential analysis and WGCNA analysis together, and GO/KEGG analysis showed that these genes were associated with lysosomal and immune/inflammatory responses. Immediately thereafter, we identified CLU, RABGEF1 and ENPEP as DLGs for DFU using three machine learning algorithms (Randomforest, SVM-RFE and LASSO) and validated their diagnostic performance in a validation cohort independent of this study. Subsequently, we constructed a novel artificial neural network model for molecular diagnosis of DFU based on DLGs, and the diagnostic performance in the training and validation cohorts was sound. In single-cell sequencing, the heterogeneous expression of DLGs also provided favorable evidence for them to be potential diagnostic targets. In addition, the results of immune infiltration analysis showed that the abundance of mainstream immune cells, including B/T cells, was down-regulated in DFUs and significantly correlated with the expression of DLGs. Finally, we found latamoxef, parthenolide, meclofenoxate, and lomustine to be promising anti-DFU drugs by targeting DLGs. CONCLUSIONS: CLU, RABGEF1 and ENPEP can be used as novel lysosomal molecular signatures of DFU, and by targeting them, latamoxef, parthenolide, meclofenoxate and lomustine were identified as promising anti-DFU drugs. The present study provides new perspectives for the diagnosis and treatment of DFU and for improving the prognosis of DFU patients.

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Background: The tertiary lymphatic structure (TLS) is an important component of the tumor immune microenvironment and has important significance in patient prognosis and response to immune therapy. However, the underlying mechanism of TLS in soft tissue sarcoma remains unclear. Methods: A total of 256 RNAseq and 7 single-cell sequencing samples were collected from TCGA-SARC and GSE212527 cohorts. Based on published TLS-related gene sets, four TLS scores were established by GSVA algorithm. The immune cell infiltration was calculated via TIMER2.0 and "MCPcounter" algorithms. In addition, the univariate, LASSO, and multivariate-Cox analyses were used to select TLS-related and prognosis-significant hub genes. Single-cell sequencing dataset, clinical immunohistochemical, and cell experiments were utilized to validate the hub genes. Results: In this study, four TLS-related scores were identified, and the total-gene TLS score more accurately reflected the infiltration level of TLS in STS. We further established two hub genes (DUSP9 and TNFSF14) prognosis markers and risk scores associated with soft tissue sarcoma prognosis and immune therapy response. Flow cytometry analysis showed that the amount of CD3, CD8, CD19, and CD11c positive immune cell infiltration in the tumor tissue dedifferentiated liposarcoma patients was significantly higher than that of liposarcoma patients. Cytological experiments showed that soft tissue sarcoma cell lines overexpressing TNFSF14 could inhibit the proliferation and migration of sarcoma cells. Conclusion: This study systematically explored the TLS and related genes from the perspectives of bioinformatics, clinical features and cytology experiments. The total-gene TLS score, risk score and TNFSF14 hub gene may be useful biomarkers for predicting the prognosis and immunotherapy efficacy of soft tissue sarcoma.

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The clinical outcomes of osteosarcoma are relatively dismal. As immunotherapy has revolutionized treatment for solid tumors, exploring novel immunotherapy-related therapeutic targets for osteosarcoma is important. In this study, we aimed to establish the connection between RNA modification and immunotherapy in osteosarcoma to identify novel therapeutic targets. An RNA modification-related signature was first developed using weight gene correlation network analysis and a machine-learning algorithm, random forest. The signature's prognostic value, drug prediction, and immune characteristics were analyzed. EIF4G2 from the signature was next identified as a critical immunotherapy determinant. EIF4G2 could also promote tumor proliferation, migration, and M2 macrophage migration by single-cell sequencing analysis and in vitro validation. Our signature and EIF4G2 are expected to provide valuable insights into the clinical management of osteosarcoma.

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Background: Psoriasis is an immune-mediated disorder influenced by environmental factors on a genetic basis. Despite advancements, challenges persist, including the diminishing efficacy of biologics and small-molecule targeted agents, alongside managing recurrence and psoriasis-related comorbidities. Unraveling the underlying pathogenesis and identifying valuable biomarkers remain pivotal for diagnosing and treating psoriasis. Methods: We employed a series of bioinformatics (including single-cell sequencing data analysis and machine learning techniques) and statistical methods to integrate and analyze multi-level data. We observed the cellular changes in psoriatic skin tissues, screened the key genes Fatty acid binding protein 5 (FABP5) and The killer cell lectin-like receptor B1 (KLRB1), evaluated the efficacy of six widely prescribed drugs on psoriasis treatment in modulating the dendritic cell-associated pathway, and assessed their overall efficacy. Finally, RT-qPCR, immunohistochemistry, and immunofluorescence assays were used to validate. Results: The regulatory influence of dendritic cells (DCs) on T cells through the CD70/CD27 signaling pathway may emerge as a significant facet of the inflammatory response in psoriasis. Notably, FABP5 and KLRB1 exhibited up-regulation and co-localization in psoriatic skin tissues and M5-induced HaCaT cells, serving as potential biomarkers influencing psoriasis development. Conclusion: Our study analyzed the impact of DC-T cell crosstalk in psoriasis, elucidated the characterization of two biomarkers, FABP5 and KLRB1, in psoriasis, and highlighted the promise and value of tofacitinib in psoriasis therapy targeting DCs.

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BACKGROUND: Glycans constitute the primary components of proteins that regulate key carcinogenic processes in cancer progression. This study investigated the significance of O-glycan synthesis in the pathogenesis, outcome, and therapy of pancreatic cancer (PC). METHODS: Transcriptomic data and clinical prognostic information of PC were acquired via TCGA and GEO databases. CSA database was used to obtain single-cell data of PC. The O-glycan biosynthesis signaling pathway and its related genes were acquired via the MSigDB platform. The nonnegative matrix factorization (NMF) clustering was utilized to construct the O-glycan biosynthesis-associated molecular subtypes in PC. The LASSO and Cox regression were utilized to build the prognostic prediction model. We utilized real-time quantitative PCR (qRT-PCR) to verify the expressed levels of model genes. Single-cell analysis was utilized to investigate the levels of target genes and O-glycan biosynthesis signaling pathway in the PC tumour microenvironment. RESULTS: : We obtained 30 genes related to O-glycan biosynthesis, among which 15 were associated with the prognosis of PC. All PC samples were grouped into two distinct molecular subtypes associated with O-glycan biosynthesis: OGRGcluster C1 and OGRGcluster C2, and compared to OGRGcluster C1. PCs in OGRGcluster C2 had a more advanced clinical stage and pathological grade, worse prognosis, and more active O-glycan biosynthesis function. Immune analysis indicated that naïve B cell, CD8+ T cell, memory-activated CD4+ T cell, and monocytes displayed remarkably higher infiltration levels in OGRGcluster C1 while resting NK cell, macrophages M0, resting dendritic cell, activated dendritic cell, and neutrophils exhibited markedly higher infiltration levels in OGRGcluster C2. OGRGcluster C1 exhibited higher sensitivities to drugs, such as cisplatin, irinotecan, KRAS(G12C) inhibitor-12, oxaliplatin, paclitaxel, and sorafenib. Besides, we built the O-glycan biosynthesis-related prognostic model (including SPRR1B, COL17A1, and ECT2) with a good prediction performance. SPRR1B, COL17A1, and ECT2 were remarkably highly expressed in PC tissues and linked to a poor outcome. Single-cell analysis revealed that O-glycan biosynthesis was observed only in PC, and consistent with this, the target genes were significantly enriched in PC. CONCLUSION: We first constructed molecular subtypes and prognostic models related to O-glycan biosynthesis in PC. It is clear that O-glycan biosynthesis is related to the development, prognosis, immune microenvironment, and treatment of PC. This provides new strategies for stratification, diagnosis, and treatment of PC patients.

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BACKGROUND: Bone marrow mesenchymal stem cells (BMSCs) have been widely recognized as a highly promising option for cell-based tissue engineering therapy targeting osteoporosis. However, the osteogenic differentiation of BMSCs is impeded by the limited viability and diminished capacity for bone formation within the osteoporotic microenvironment. METHODS: In this study, the COL6A3 gene was confirmed through an extensive analysis of the preceding single-cell sequencing database. The generation of an inflammatory microenvironment resembling osteoporotic cell transplantation was achieved by employing lipopolysaccharide (LPS). A lentivirus targeting the COL6A3 gene was constructed, and a Western blotting assay was used to measure the marker proteins of osteogenesis, adipogenesis, and mitophagy. Immunofluorescence was utilized to observe the colocalization of mitochondria and lysosomes. The apoptosis rate of each group was evaluated using the TUNEL assay, and the mitochondrial membrane potential was assessed using JC-1 staining. RESULTS: This investigation discovered that the impaired differentiation capacity and decreased viability of BMSCs within the inflammatory microenvironment were markedly ameliorated upon overexpression of the specific COL6A3 gene. Moreover, the administration of COL6A3 gene overexpression successfully mitigated the inhibitory impacts of LPS on mitophagy and the expression of inflammatory mediators, specifically inducible nitric oxide synthase (iNOS) and cyclooxygenase-2 (COX-2), in BMSCs. To clarify the underlying mechanism, the role of mitophagy during the differentiation of COL6A3 gene-modified BMSCs in the inflammatory microenvironment was evaluated using the mitophagy inhibitor Mdivi-1. CONCLUSIONS: In the context of lipopolysaccharide (LPS) stimulation, COL6A3 enhances the differentiation of BMSCs into osteogenic and adipogenic lineages through the promotion of mitophagy and the maintenance of mitochondrial health. Our findings may provide a novel therapeutic approach utilizing stem cells in the treatment of osteoporosis.

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OBJECTIVE: Bone morphogenetic protein (BMP) signaling is intricately involved in adipose tissue development. BMP7 together with BMP4 have been implicated in brown adipocyte differentiation but their roles during development remains poorly specified. Matrix Gla protein (MGP) inhibits BMP4 and BMP7 and is expressed in endothelial and progenitor cells. The objective was to determine the role of MGP in brown adipose tissue (BAT) development. METHODS: The approach included global and cell-specific Mgp gene deletion in combination with RNA analysis, immunostaining, thermogenic activity, and in vitro studies. RESULTS: The results revealed that MGP directs brown adipogenesis at two essential steps. Endothelial-derived MGP limits triggering of white adipogenic differentiation in the perivascular region, whereas MGP derived from adipose cells supports the transition of CD142-expressing progenitor cells to brown adipogenic maturity. Both steps were important to optimize the thermogenic function of BAT. Furthermore, MGP derived from both sources impacted vascular growth. Reduction of MGP in either endothelial or adipose cells expanded the endothelial cell population, suggesting that MGP is a factor in overall plasticity of adipose tissue. CONCLUSION: MGP displays a dual and cell-specific function in BAT, essentially creating a "cellular shuttle" that coordinates brown adipogenic differentiation with vascular growth during development.

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The currently predominant approach to transcriptomic and epigenomic single-cell analysis depends on a rigid perspective constrained by reduced dimensions and algorithmically derived and annotated clusters. Here, we developed Seqtometry (sequencing-to-measurement), a single-cell analytical strategy based on biologically relevant dimensions enabled by advanced scoring with multiple gene sets (signatures) for examination of gene expression and accessibility across various organ systems. By utilizing information only in the form of specific signatures, Seqtometry bypasses unsupervised clustering and individual annotations of clusters. Instead, Seqtometry combines qualitative and quantitative cell-type identification with specific characterization of diverse biological processes under experimental or disease conditions. Comprehensive analysis by Seqtometry of various immune cells as well as other cells from different organs and disease-induced states, including multiple myeloma and Alzheimer's disease, surpasses corresponding cluster-based analytical output. We propose Seqtometry as a single-cell sequencing analysis approach applicable for both basic and clinical research.

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BACKGROUND: Glioblastoma (GBM) is a highly aggressive cancer with heavy mortality rates and poor prognosis. Cellular senescence exerts a pivotal influence on the development and progression of various cancers. However, the underlying effect of cellular senescence on the outcomes of patients with GBM remains to be elucidated. METHODS: Transcriptome RNA sequencing data with clinical information and single-cell sequencing data of GBM cases were obtained from CGGA, TCGA, and GEO (GSE84465) databases respectively. Single-sample gene set enrichment analysis (ssGSEA) analysis was utilized to calculate the cellular senescence score. WGCNA analysis was employed to ascertain the key gene modules and identify differentially expressed genes (DEGs) associated with the cellular senescence score in GBM. The prognostic senescence-related risk model was developed by least absolute shrinkage and selection operator (LASSO) regression analyses. The immune infiltration level was calculated by microenvironment cell populations counter (MCPcounter), ssGSEA, and xCell algorithms. Potential anti-cancer small molecular compounds of GBM were estimated by "oncoPredict" R package. RESULTS: A total of 150 DEGs were selected from the pink module through WGCNA analysis. The risk-scoring model was constructed based on 5 cell senescence-associated genes (CCDC151, DRC1, C2orf73, CCDC13, and WDR63). Patients in low-risk group had a better prognostic value compared to those in high-risk group. The nomogram exhibited excellent predictive performance in assessing the survival outcomes of patients with GBM. Top 30 potential anti-cancer small molecular compounds with higher drug sensitivity scores were predicted. CONCLUSION: Cellular senescence-related genes and clusters in GBM have the potential to provide valuable insights in prognosis and guide clinical decisions.

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BACKGROUND: Cancer-associated fibroblasts (CAFs) play a significant role in regulating the clinical outcome and radiotherapy prognosis of prostate cancer (PCa). The aim of this study is to identify CAFs-related genes (CAFsRGs) using single-cell analysis and evaluate their potential for predicting the prognosis and radiotherapy prognosis in PCa. METHODS: We acquire transcriptome and single-cell RNA sequencing (scRNA-seq) results of PCa and normal adjacent tissues from The GEO and TCGA databases. The "MCPcounter" and "EPIC" R packages were used to assess the infiltration level of CAFs and examine their correlation with PCa prognosis. ScRNA-seq and differential gene expression analyses were used to extract CAFsRGs. We also applied COX and LASSO analysis to further construct a risk score (CAFsRS) to assess biochemical recurrence-free survival (BRFS) and radiotherapy prognosis of PCa. The predictive efficacy of CAFsRS was evaluated by ROC curves and subgroup analysis. Finally, we integrated the CAFsRS gene signature with relevant clinical features to develop a nomogram, enhancing the predictive accuracy. RESULTS: The abundance of CAFs is associated with a poor prognosis of PCa patients. ScRNA-seq and differential gene expression analysis revealed 323 CAFsRGs. After COX and LASSO analysis, we obtained seven CAFsRGs with prognostic significance (PTGS2, FKBP10, ENG, CDH11, COL5A1, COL5A2, and SRD5A2). Additionally, we established a risk score model based on the training set (n = 257). The ROC curve was used to confirm the performance of CAFsRS (The AUC values for 1, 3 and 5-year survival were determined to be 0.732, 0.773, and 0.775, respectively.). The testing set (n = 129), GSE70770 set (n = 199) and GSE116918 set (n = 248) revealed that the model exhibited exceptional predictive performance. This was also confirmed by clinical subgroup analysis. The violin plot demonstrated a statistically significant disparity in the CAFs infiltrations between the high-risk and low-risk groups of CAFsRS. Further analysis confirmed that both CAFsRS and T stage were independent prognostic factors for PCa. The nomogram was then established and its excellent predictive performance was demonstrated through calibration and ROC curves. Finally, we developed an online prognostic prediction app ( https://sysu-symh-cafsnomogram.streamlit.app/ ) to facilitate the practical application of the nomogram. CONCLUSIONS: The prognostic prediction risk score model we constructed could accurately predict BRFS and radiotherapy prognosis PCa, which can provide new ideas for clinicians to develop personalized PCa treatment and follow-up programs.

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Background: The liver is the major metabolic organ of the human body, and abnormal metabolism is the main factor influencing hepatocellular carcinoma (HCC). This study was designed to determine the effect of glutamine metabolism on HCC heterogeneity and to develop a prognostic evaluator based on the heterogeneity study of glutamine metabolism within HCC tumors and between tissues. Methods: Single-cell transcriptome data were extracted from the GSE149614 dataset and processed using the Seurat package in R for quality control of these data. HCC subtypes in the Cancer Genome Atlas and the GSE14520 dataset were identified via consensus clustering based on glutamine family amino acid metabolism (GFAAM) process genes. The machine learning algorithms gradient boosting machine, support vector machine, random forest, eXtreme gradient boosting, decision trees, and least absolute shrinkage and selection operator were utilized to develop the prognosis model of differentially expressed genes among the molecular gene subtypes. Results: The samples in the GSE149614 dataset included 10 cell types, and there was no significant difference in the GFAAM pathway. HCC was classified into three molecular subtypes according to GFAAM process genes, showing molecular heterogeneity in prognosis, clinicopathological features, and immune cell infiltration. C1 showed the worst survival rate and the highest immune score and immune cell infiltration. A six-gene model for prognostic and immunotherapy responses was constructed among subtypes, and the calculated high-risk score was significantly correlated with poor prognosis, high immune abundance, and a low response rate of immunotherapy in HCC. Conclusion: Our discovery of GFAAM-associated marker genes may help to further decipher the role in HCC occurrence and progression. In particular, this six-gene prognostic model may serve as a predictor of treatment and prognosis in HCC patients.

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BACKGROUND: Recent studies have shown that lysosomes not only provide energy for tumor cell growth, but also participate in the occurrence and development of malignant tumors by regulating various ways of tumor cell death. However, the role of lysosome associated genes (LSAGs) in hepatocellular carcinoma (HCC) remains unclear. METHODS: Transcriptome data and clinical data of HCC were downloaded from the Cancer Genome Atlas (TCGA) and the International Cancer Genome Consortium (ICGC) databases. We identified differential expression of LSAGs by comparing tumor tissue with normal liver tissue. Subsequently, we used univariate COX analysis and least absolute shrinkage and selection operator (LASSO) COX regression to construct the prognostic feature of LSAGs. Kaplan-Meier survival curve and receiver operating characteristic curve were used to evaluate the predictive ability of LSAGs feature. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) were used for functional enrichment analysis of risk differential genes. The relationship between LSAGs score and tumor microenvironment and chemotherapy drug sensitivity was analyzed. Finally, the cellular communication of tumor cells with high and low expression of model LSAGs was explored. RESULTS: We identified sixteen prognostic associated LSAGs, four of which were selected to construct prognostic feature of LSAGs. Patients in the low LSAGs group had a better prognosis than those in the high LSAGs group. GO and KEGG analyses showed that risk differential genes were enriched in leukocyte migration, cytokine-cytokine receptor interaction and PI3K-Akt signaling pathway. The group with low LSAGs score had lower immune score. Patients in the high LSAGs group were more sensitive to drugs for chemotherapy. In addition, tumor cells with high expression of model LSAGs showed stronger association with immune cells through the interleukin-2 (IL2), fibroblast growth factor (FGF), adiponectin, and bone morphogenetic proteins (BMP) signaling pathways. CONCLUSION: We established a LSAGs signature that had the ability to predict clinical prognosis and immune landscape, proposing potential therapeutic targets for HCC.

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BACKGROUND: Renal ischemia-reperfusion injury (IRI) is a serious clinical complication of kidney injury. This research dealt with investigating the hub genes and pathways associated with renal IRI. METHODS: The transcriptome expression dataset of mouse renal ischemia samples (GSE39548) was obtained from the Gene Expression Omnibus (GEO) database. The differentially expressed genes (DEGs) were filtered by R software for key genes utilized for gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis, and gene enrichment analysis (GSEA). The gene co-expression network was developed by WGCNA analysis to screen important modules. Hub genes from the intersection of DEGs and WGCNA were subjected to protein-protein interaction (PPI) network. The biomarkers obtained by SVM-REF and LASSO algorithm were validated by other datasets and subjected to GSEA analysis. The expression of biomarkers in renal IRI was detected by qRT-PCR and subjected to single-cell analysis. RESULTS: A total of 157 DEGs were discovered. Biological function analysis depicted that the DEGs were primarily involved in cytokine-cytokine receptor interaction, as well as the signaling pathways IL-17, MAPK, and TNF. The intersection of DEGs and the genes obtained by WGCNA analysis yielded 149 hubs genes. Based on SVM-REF and LASSO algorithm, cyp1a1 and pdk4 were determined as potential biomarkers in individuals with renal ischemia and showed good diagnostic value. qRT-PCR results depicted that cyp1a1 and pdk4 were significantly up-regulated in renal ischemia mice (P < 0.05). Finally, the single-cell analysis identified the expression of Cyp1a1 and Pdk4 in mice kidney tissue. CONCLUSION: cyp1a1 and pdk4 were identified to play important roles in renal IRI. This research provides a new perspective and basis for studying the pathogenesis of renal IRI and developing new treatments.

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Background: Heart failure is associated with shifts in substrate preferences and energy insufficiency. Although cardiac metabolism has been explored at the organ level, the metabolic changes at the individual cell level remain unclear. This study employed single-cell ribonucleic acid (RNA) sequencing to investigate the cell-type-specific characteristics of gene expression related to fatty acid metabolism. Methods: Single-cell RNA sequencing data from fetal hearts were processed to analyze gene expression patterns related to fatty acid metabolism. Immunofluorescence staining and Western blotting techniques were employed to validate the expression of specific proteins. Additionally, calcium recording and contractility measurements were performed to assess the functional implications of fatty acid metabolism in cardiomyocytes. Results: Based on single-cell RNA sequencing data analysis, we found that a decrease in overall energy requirements underlies the downregulation of fatty acid oxidation-related genes in the later period of heart maturation and the compensatory increase of fatty acid metabolism in individual cardiomyocytes during heart failure. Furthermore, we found that solute carrier family 27 member 6 (SLC27A6), a fatty acid transport protein, is involved in cardiac maturation. SLC27A6 knockdown in human induced pluripotent stem cell-derived cardiomyocytes resulted in an immature cardiomyocyte transcriptional profile, abnormal morphology, impaired Ca2+ handling activity, and contractility. Conclusions: Overall, our study offers a novel perspective for exploring cardiac fatty acid metabolism in fetal and failing hearts along with new insights into the cellular mechanism underlying fatty acid metabolic alterations in individual cardiac cells. It thus facilitates further exploration of cardiac physiology and pathology.

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Currently, the role of liquid-liquid phase separation (LLPS) in cancer has been preliminarily explained. However, the significance of LLPS in breast cancer is unclear. In this study, single cell sequencing datasets GSE188600 and GSE198745 for breast cancer were downloaded from the GEO database. Transcriptome sequencing data for breast cancer were downloaded from UCSC database. We divided breast cancer cells into high-LLPS group and low-LLPS group by down dimension clustering analysis of single-cell sequencing data set, and obtained differentially expressed genes between the two groups. Subsequently, weighted co-expression network analysis (WGCNA) was performed on transcriptome sequencing data, and the module genes most associated with LLPS were obtained. COX regression and Lasso regression were performed and the prognostic model was constructed. Subsequently, survival analysis, principal component analysis, clinical correlation analysis, and nomogram construction were used to evaluate the significance of the prognostic model. Finally, cell experiments were used to verify the function of the model's key gene, PGAM1. We constructed a LLPS-related prognosis model consisting of nine genes: POLR3GL, PLAT, NDRG1, HMGB3, HSPH1, PSMD7, PDCD2, NONO and PGAM1. By calculating LLPS-related risk scores, breast cancer patients could be divided into high-risk and low-risk groups, with the high-risk group having a significantly worse prognosis. Cell experiments showed that the activity, proliferation, invasion and healing ability of breast cancer cell lines were significantly decreased after knockdown of the key gene PGAM1 in the model. Our study provides a new idea for prognostic stratification of breast cancer and provides a novel marker: PGAM1.

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Lung adenocarcinoma (LUAD) emerges as one of the most aggressive tumor types with a poor prognosis. As a novel form of regulated cell death, ferroptosis promotes the clearance of tumor cells. However, few studies demonstrated whether ferroptosis-related genes can modify the behavior of tumor microenvironment (TME) cells. Resorting to non-negative matrix factorization (NMF) clustering based on the expression of ferroptosis-related genes, we identified multiple LUAD TME cell-type subpopulations. These subtypes of TME cells displayed extensive communication with tumor epithelial cells. ATF3+cancer-associated fibroblasts (CAFs), SLC40A1+CD8+T cells, and ALOX5+CD8+T cells showed distinct biological features compared to non-ferroptosis-related TME cells. Patients with a higher abundance of these ferroptosis-related TME cell subtypes showed a favorable clinical outcome. Our study depicted a detailed landscape of LUAD cell composition with a focus on ferroptosis-related genes, which, hopefully, may provide novel insight into further study of the LAUD immune microenvironment.

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Background: There is growing evidence that immune cells are strongly associated with the prognosis and treatment of clear cell renal cell carcinoma (ccRCC). Our aim is to construct an immune subtype-related model to predict the prognosis of ccRCC patients and to provide guidance for finding appropriate treatment strategies. Methods: Based on single-cell analysis of the GSE152938 dataset from the GEO database, we defined the immune subtype-related genes in ccRCC. Immediately afterwards, we used Cox regression and Lasso regression to build a prognostic model based on TCGA database. Then, we carried out a series of evaluation analyses around the model. Finally, we proved the role of VMP1 in ccRCC by cellular assays. Result: Initially, based on TCGA ccRCC patient data and GEO ccRCC single-cell data, we successfully constructed a prognostic model consisting of five genes. Survival analysis showed that the higher the risk score, the worse the prognosis. We also found that the model had high predictive accuracy for patient prognosis through ROC analysis. In addition, we found that patients in the high-risk group had stronger immune cell infiltration and higher levels of immune checkpoint gene expression. Finally, cellular experiments demonstrated that when the VMP1 gene was knocked down, 786-O cells showed reduced proliferation, migration, and invasion ability and increased levels of apoptosis. Conclusion: Our study can provide a reference for the diagnosis and treatment of patients with ccRCC.