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Multi-omics and Mendelian randomization identify S1PR5 as a causal protective gene and NK cell-mediated prognostic biomarker in lung adenocarcinoma.
BACKGROUND: Lung adenocarcinoma (LUAD) sustains an immunosuppressive tumor microenvironment (TME) via stromal-immune interactions. Efferocytosis regulates immune suppression and tissue homeostasis, yet biomarkers stratifying its TME states are lacking in LUAD, hindering precision therapy. This study aimed to investigate efferocytosis-associated immune regulation with both causal and prognostic relevance in LUAD, and to identify key biomarkers with potential implications for tumor stratification and therapeutic guidance. METHODS: Gene expression profiles from The Cancer Genome Atlas (TCGA) LUAD cohort and Genotype-Tissue Expression (GTEx) underwent differential expression analysis. Efferocytosis-related genes (ERGs) from GeneCards were intersected to identify LUAD-associated candidates. Mendelian randomization (MR) and colocalization evaluated causal ERG-LUAD relationships. Risk-related ERGs were systematically analyzed for expression, biological functions, prognosis, and immune interactions. Single-cell RNA sequencing (scRNA-seq) mapped cellular expression specificity of core ERGs. Machine learning prognostic models (S1PR5-enriched NK cell-related prognostic signature, SENRPS) were developed and validated across independent cohorts. RESULTS: S1PR5 expression was significantly lower in tumor tissues from LUAD patients compared to healthy lung tissue, at both the transcript and protein levels. We identified S1PR5 as a dual biomarker serving both as a protective factor against LUAD pathogenesis and a prognostic marker for survival outcomes, linked to favorable prognosis and enhanced therapy sensitivity. ScRNA-seq localized S1PR5 to natural killer (NK) cells, enhancing the anti-tumor activity of CD16+ NK cells and mediating interactions with antigen-presenting CEACAM8+ macrophages. The SENRPS model integrates molecular and cellular features for risk stratification and clinical decision-making. CONCLUSIONS: S1PR5 serves as a causal protective gene and prognostic biomarker governing cytotoxic immunity. SENRPS bridges TME dynamics to clinical risk prediction and therapeutic optimization, advancing LUAD precision oncology.
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Analysis of cuproptosis in hepatocellular carcinoma using multi-omics reveals a comprehensive HCC landscape and the immune patterns of cuproptosis.
Cuproptosis represents a novel copper-dependent regulated cell death, distinct from other known cell death processes. In this report, a comprehensive analysis of cuproptosis in hepatocellular carcinoma (HCC) was conducted using multi-omics including genomics, bulk RNA-seq, single cell RNA-seq and proteomics. ATP7A, PDHA1 and DLST comprised the top 3 mutation genes in The Cancer Genome Atlas (TCGA)-LIHC; 9 cuproptosis-related genes showed significant, independent prognostic values. Cuproptosis-related hepatocytes were identified and their function were evaluated in single cell assays. Based on cuproptosis-related gene expressions, two immune patterns were found, with the cuproptosis-C1 subtype identified as a cytotoxic immune pattern, while the cuproptosis-C2 subtype was identified as a regulatory immune pattern. Cuproptosis-C2 was associated with a number of pathways involving tumorigenesis. A prognosis model based on differentially expressed genes (DEGs) of cuproptosis patterns was constructed and validated. We established a cuproptosis index (CPI) and further performed an analysis of its clinical relevance. High CPI values were associated with increased levels of alpha-fetoprotein (AFP) and advanced tumor stages. Taken together, this comprehensive analysis provides important, new insights into cuproptosis mechanisms associated with human HCC.
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Exploring the Shared Gene Signatures and Molecular Mechanisms between Bladder Urothelial Carcinoma and Metabolic Syndrome.
BACKGROUND: The aim of this study was to investigate the common gene signatures and potential molecular mechanisms of bladder urothelial carcinoma (BLCA) and metabolic syndrome (MS). METHODS: Transcriptome data for BLCA and MS were obtained from the Gene Expression Omnibus (GEO) database. Weighted gene co-expression network analysis (WGCNA) was utilized to identify co-expression networks associated with BLCA and MS, and five hub genes were further screened and validated using logistic least absolute shrinkage and selection operator (LASSO) regression models and receiver operating characteristic (ROC) curve, and external dataset for validation. The relationship between the hub genes and the clinicopathological characteristics and prognosis of BLCA patients was explored in the GEO and The Cancer Genome Atlas (TCGA)-BLCA cohorts, respectively. Differences in the immune microenvironment of BLCA and MS were analyzed using the database CIBERSORT and the R package "ssGSEA", and the correlation between hub genes and tumor microenvironment, immune score and targeted drugs was analyzed with the help of the TCGA-BLCA cohort. Finally, BLCA single-cell RNA (scRNA) data were used to analyze the expression levels of the hub genes in various cell types of BLCA and molecular mechanisms. RESULTS: Five hub genes were screened by WGCNA and LASSO regression analysis, namely AP2-associated protein kinase 1 (AAK1), ATP-binding cassette subfamily F member 2 (ABCF2), Mitochondrial ribosomal protein L42 (MRPL42), La-related protein 3 (SSB) and TATA-box binding protein-associated factor 10 (TAF10). Analyzed in the GEO and TCGA-BLCA cohorts, we found that the hub genes (TAF10 and ABCF2) were closely associated with the clinicopathological characteristics and prognosis of BLCA patients. In CIBERSORT, we discovered that the hub genes are closely linked to the immune microenvironment, immune score, and especially with dendritic cells (DCs). In the single-cell RNA sequencing (scRNA-seq) analysis of BLCA, we identified that SSB was significantly differentially expressed in BLCA and normal bladder tissues and that it plays an important role in the development of BLCA. CONCLUSIONS: The interaction of BLCA with MS may be associated with several cancer pathways being activated and identified TAF10 and ABCF2 as potential biomarkers and therapeutic targets for patients with BLCA and MS.
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HMOX1: A pivotal regulator of prognosis and immune dynamics in ovarian cancer.
BACKGROUND: This study investigates the intricate role of Heme Oxygenase 1 (HMOX1) in ovarian cancer, emphasizing its prognostic significance, influence on immune cell infiltration, and impact on the malignant characteristics of primary ovarian cancer cells. MATERIALS AND METHODS: Our research began with an analysis of HMOX1 expression and its prognostic implications using data from The Cancer Genome Atlas (TCGA) dataset, supported by immunohistochemical staining. Further analyses encompassed co-expression studies, Gene Ontology (GO) annotations, and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment. We utilized the TIMER and TISIDB platforms to evaluate the immunotherapeutic potential of HMOX1. Additionally, in vitro studies that involved modulating HMOX1 levels in primary ovarian cancer cells were conducted to confirm its biological functions. RESULTS: Our findings indicate a significant overexpression of HMOX1 in ovarian cancer, which correlates with increased tumor malignancy and poorer prognosis. HMOX1 was shown to significantly modulate the infiltration of immune cells, particularly neutrophils and macrophages. Single-cell RNA sequencing (scRNA-seq) analysis revealed that HMOX1 is predominantly expressed in tumor-associated macrophages (TAMs), with a positive correlation to chemokines and their receptors. An increase in HMOX1 levels was associated with heightened levels of immunoinhibitors, immunostimulators, and MHC molecules. Functional assays demonstrated that HMOX1 knockdown promotes apoptosis, attenuating cell proliferation and invasion, while its overexpression yields opposing effects. CONCLUSION: HMOX1 emerges as a critical therapeutic target, intricately involved in immunomodulation, prognosis, and the malignant behavior of ovarian cancer. This highlights HMOX1 as a potential biomarker and therapeutic target in the fight against ovarian cancer.
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Identification of hepatocellular carcinoma-related subtypes and development of a prognostic model: a study based on ferritinophagy-related genes.
BACKGROUND: Hepatocellular carcinoma still has a high incidence and mortality rate worldwide, and further research is needed to investigate its occurrence and development mechanisms in depth in order to identify new therapeutic targets. Ferritinophagy is a type of autophagy and a key factor in ferroptosis that could influence tumor onset and progression. Although, the potential role of ferritinophagy-related genes (FRGs) in liver hepatocellular carcinoma (LIHC) is unknown. METHODS: Single-cell RNA sequencing (scRNA-seq) data of LIHC were obtained from the Gene Expression Omnibus (GEO) dataset. In addition, transcriptome and clinical follow-up outcome data of individuals with LIHC were extracted from the The Cancer Genome Atlas (TCGA) dataset. FRGs were collected through the GeneCards database. Differential cell subpopulations were distinguished, and differentially expressed FRGs (DEFRGs) were obtained. Differential expression of FRGs and prognosis were observed according to the TCGA database. An FRG-related risk model was constructed to predict patient prognosis by absolute shrinkage and selection operator (LASSO) and COX regression analyses, and its prognosis predictive power was validated. Ultimately, the association between risk score and tumor microenvironment (TME), immune cell infiltration, immune checkpoints, drug sensitivity, and tumor mutation burden (TMB) was analyzed. We also used quantitative reverse transcription polymerase chain reaction (qRT-PCR) to validate the expression of key genes in normal liver cells and liver cancer cells. RESULTS: We ultimately identified 8 cell types, and 7 differentially expressed FRGs genes (ZFP36, NCOA4, FTH1, FTL, TNF, PCBP1, CYB561A3) were found among immune cells, and we found that Monocytes and Macrophages were closely related to FRGs genes. Subsequently, COX regression analysis showed that patients with high expression of FTH1, FTL, and PCBP1 had significantly worse prognosis than those with low expression, and our survival prediction model, constructed based on age, stage, and risk score, showed better prognostic prediction ability. Our risk model based on 3 FRGs genes ultimately revealed significant differences between high-risk and low-risk groups in terms of immune infiltration and immune checkpoint correlation, drug sensitivity, and somatic mutation risk. Finally, we validated the key prognostic genes FTH1, FTL, using qRT-PCR, and found that the expression of FTH1 and FTL was significantly higher in various liver cancer cells than in normal liver cells. At the same time, immunohistochemistry showed that the expression of FTH1, FTL in tumor tissues was significantly higher than that in para-tumor tissues. CONCLUSION: This study identifies a considerable impact of FRGs on immunity and prognosis in individuals with LIHC. The collective findings of this research provide new ideas for personalized treatment of LIHC and a more targeted therapy approach for individuals with LIHC to improve their prognosis.
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Integrated multi-omics analysis and machine learning identify G protein-coupled receptor-related signatures for diagnosis and clinical benefits in soft tissue sarcoma.
BACKGROUND: G protein-coupled receptors (GPRs) are associated with tumor development and prognosis. However, there were fewer reports of GPR-related signatures (GPRSs) in soft tissue sarcomas (STSs), and we aim to combine GPR-related genes with cellular landscape to construct diagnostic and prognostic models in STSs. METHODS: Based on AddModuleScore, single-sample gene set enrichment analysis (ssGSEA), differentially expressed genes (DEGs), and weighted gene co-expression network analysis (WGCNA), GPR-related genes (GPRs) were screened at both the single-cell and bulk RNA-seq levels based on The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. We developed a novel machine learning framework that incorporated 12 machine learning algorithms and their 127 combinations to construct a consensus GPRS to screen biomarkers with diagnostic significance and clinical translation, which was assessed by the internal and external validation datasets. Moreover, the GPR-TME classifier as the prognosis model was constructed and further performed for immune infiltration, functional enrichment, somatic mutation, immunotherapy response prediction, and scRNA-seq analyses. RESULTS: We identified 151 GPR-related genes at both the single-cell and bulk transcriptome levels, and identified a Stepglm[both]+Enet[alpha=0.6] model with seven GPR-related genes as the final diagnostic predictive model with high accuracy and translational relevance using a 127-combination machine learning computational framework, and the GPR-integrated diagnosis nomogram provided a quantitative tool in clinical practice. Moreover, we identified seven prognosis GPRs and five prognosis-good immune cells constructing the GPR score and TME score, respectively. The findings indicate that high expression of GPRs is associated with a poor prognosis in patients with STS, highlighting the significant role of GPRs and the tumor microenvironment (TME) in STS development. Building up a GPR-TME classifier, low GPR combined with high TME exhibited the most favorable prognosis and immunotherapeutic efficacy, which was further performed for immune infiltration, functional enrichment, somatic mutation, immunotherapy response prediction, and scRNA-seq analyses. CONCLUSIONS: Our study constructed a GPRS that can serve as a promising tool for diagnosis and prognosis prediction, targeted prevention, and personalized medicine in STS.
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A Gene Signature Predicting Natural Killer Cell Infiltration and Improved Survival in Melanoma Patients.
Natural killer (NK) cell activity is essential for initiating antitumor responses and may be linked to immunotherapy success. NK cells and other innate immune components could be exploitable for cancer treatment, which drives the need for tools and methods that identify therapeutic avenues. Here, we extend our gene-set scoring method singscore to investigate NK cell infiltration by applying RNA-seq analysis to samples from bulk tumors. Computational methods have been developed for the deconvolution of immune cell types within solid tumors. We have taken the NK cell gene signatures from several such tools, then curated the gene list using a comparative analysis of tumors and immune cell types. Using a gene-set scoring method to investigate RNA-seq data from The Cancer Genome Atlas (TCGA), we show that patients with metastatic cutaneous melanoma have an improved survival rate if their tumor shows evidence of NK cell infiltration. Furthermore, these survival effects are enhanced in tumors that show higher expression of genes that encode NK cell stimuli such as the cytokine IL15 Using this signature, we then examine transcriptomic data to identify tumor and stromal components that may influence the penetrance of NK cells into solid tumors. Our results provide evidence that NK cells play a role in the regulation of human tumors and highlight potential survival effects associated with increased NK cell activity. Our computational analysis identifies putative gene targets that may be of therapeutic value for boosting NK cell antitumor immunity.
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Regulatory T cell-associated gene signature correlates with prognostic risk and immune infiltration in patients with breast cancer.
BACKGROUND: Regulatory T cells (Tregs) play a pivotal role in the development, prognosis, and treatment of breast cancer. This study aimed to develop a Treg-associated gene signature that contributes to predict prognosis and therapy benefits in breast cancer. METHODS: Treg-associated genes were screened based on single-cell RNA-sequencing (RNA-seq) in TISCH2 database and the bulk RNA-seq in The Cancer Genome Atlas (TCGA) database. Treg-associated gene signature was identified via survival analysis, univariate cox, least absolute shrinkage and selection operator (LASSO) and multivariable Cox regression analyses. Immune status was assessed using single-sample gene set enrichment analysis (ssGSEA) and Estimation of STromal and Immune cells in MAlignant Tumor tissues using Expression data (ESTIMATE) algorithms. Drug sensitivity was estimated using pRRophetic. Gene set enrichment analysis (GSEA) was conducted to explore the changed pathways. RESULTS: A total of 169 genes were identified as Treg-associated genes, and close interactions existed among these genes. Kaplan-Meier (KM) survival and univariate cox revealed 29 prognostic genes (all P<0.05), and finally a six-gene prognostic signature including TBC1D4, PMAIP1, IFNG, LEF1, MZB1 and EZR was identified by LASSO and multivariable Cox. Based on this signature, patients in high-risk group exhibited a worse survival probability than those in low-risk group in the TCGA training dataset (P<0.001). Additionally, this signature showed a moderate predictive power for 1-, 3- and 5-year survival for breast cancer patients in both training dataset [area under the curve (AUC) =0.705, 0.678 and 0.668, respectively]. Similar predictive power for 1-, 3- and 5-year survival was also observed in validation datasets. Risk scores significantly differed between subgroups divided by clinicopathologic features, especially by molecular subtypes. Patients in high- and low-risk groups showed significant differences on infiltration abundance of multiple types of immune cells (such as, activated B cells/CD8+ T cells/CD4+ T cells), immune and stromal scores (all P<0.05). Moreover, sensitivity to 83 chemotherapeutic drugs such as lapatinib, methotrexate, and gefitinib were significantly differed between the two risk groups (all P<0.001). CONCLUSIONS: This is the first to develop a Treg-associated gene signature for breast cancer, which could predict prognosis of patients and help to identify patients who might be benefit from immunotherapy and/or chemotherapy.
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Big data analysis reveals miR-874 as a suppressor and therapeutic target in the progression from endometriosis to ovarian cancer.
BACKGROUND: Endometriosis (EMS) is a common chronic gynecological disorder that increases the risk of ovarian cancer by approximately threefold. However, the molecular mechanisms underlying this association remain unclear. METHODS: Mendelian randomization analysis was used to identify the causal relationship between EMS and ovarian cancer. Differential gene expression analysis was conducted using the cancer genome atlas (TCGA) dataset, grouping samples based on miR-874 expression. EcoTyper analysis was applied to evaluate tumor microenvironment composition concerning miR-874 expression. Weighted gene co-expression network analysis (WGCNA) and miWalk target prediction were used to identify key regulatory targets of miR-874. Single-cell RNA sequencing (scRNA-seq) analysis and ChIP-Seq data mining further elucidated the role of miR-874 in clear cell ovarian cancer (CCOC) pathogenesis. RESULTS: EMS is a key risk factor for CCOC. Multi-datasets analysis revealed that miR-874 down-regulation is closely associated with EMS occurrence, ovarian cancer progression, and patient survival. A total of 194 differentially expressed genes were identified between the high and low miR-874 expression groups, with key genes such as ASB9, TMSB10P1, SMIM24, and PDLIM4 showing significant changes. Functional analysis revealed enrichment in pathways related to mitochondrial function, cell adhesion, proteasomal degradation, and p53 signaling. Survival analysis demonstrated that down-regulation of genes in the high miR-874 group (ZNF623, NDRG1, ZNF7, MTBP, and PHF20L1) was associated with better prognosis, while up-regulation of genes (PDLIM4 and ECRG4) correlated with poorer survival. EcoTyper analysis identified significant tumor microenvironment changes in the high miR-874 group, with an increased abundance of myoepithelial-like (Endothelial.cells.5) and myofibroblast-like (Fibroblasts.1) cells, and reduced levels of migratory-like fibroblasts (Fibroblasts.8), suggesting a more benign tumor phenotype. WGCNA and miWalk analyses indicated that ZNF217 and NDRG1 are potential regulatory targets of miR-874. scRNA-seq data suggested that ZNF217 and NDRG1 are predominantly expressed in ovarian stromal and immune cells. Further, ChIP-Seq data suggest that ZNF217 may bind to the MTBP promoter, which is associated with the stabilization of MDM2, a key negative regulator of p53. We propose a hypothetical miR-874–ZNF217–MTBP–MDM2–p53 regulatory axis that may contribute to the progression of CCOC. CONCLUSION: The down-regulation of miR-874 may be involved in the progression from EMS to CCOC. We propose that miR-874 may act as a tumor suppressor in CCOC, potentially through the miR-874–ZNF217–MTBP–MDM2–p53 regulatory axis. This proposed mechanism warrants further experimental validation. Collectively, these results provide preliminary insights into the molecular pathogenesis of EMS-associated CCOC and may inform future efforts to identify prognostic markers and therapeutic targets.
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High-throughput full-length single-cell RNA-seq automation.
Existing protocols for full-length single-cell RNA sequencing produce libraries of high complexity (thousands of distinct genes) with outstanding sensitivity and specificity of transcript quantification. These full-length libraries have the advantage of allowing probing of transcript isoforms, are informative regarding single-nucleotide polymorphisms and allow assembly of the VDJ region of the T- and B-cell-receptor sequences. Since full-length protocols are mostly plate-based at present, they are also suited to profiling cell types where cell numbers are limiting, such as rare cell types during development. A disadvantage of these methods has been the scalability and cost of the experiments, which has limited their popularity as compared with droplet-based and nanowell approaches. Here, we describe an automated protocol for full-length single-cell RNA sequencing, including both an in-house automated Smart-seq2 protocol and a commercial kit-based workflow. The protocols take 3-5 d to complete, depending on the number of plates processed in a batch. We discuss these two protocols in terms of ease of use, equipment requirements, running time, cost per sample and sequencing quality. By benchmarking the lysis buffers, reverse transcription enzymes and their combinations, we have optimized the in-house automated protocol to dramatically reduce its cost. An automated setup can be adopted easily by a competent researcher with basic laboratory skills and no prior automation experience. These pipelines have been employed successfully for several research projects allied with the Human Cell Atlas initiative ( www.humancellatlas.org ).