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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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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 ).
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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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A novel immune-related gene signature stratifies prognosis and characterizes the tumor immune microenvironment in head and neck squamous cell carcinoma.
BACKGROUND: Head and neck squamous cell carcinoma (HNSCC) exhibits heterogeneous responses to immunotherapy, primarily determined by the tumor immune microenvironment (TME). Accordingly, there is an urgent need for reliable biomarkers that can effectively distinguish between immune "hot" and "cold" tumors and predict prognosis and immunotherapy response. METHODS: We integrated transcriptomic and clinical data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) cohorts to identify immune-related genes (IRGs) associated with TME phenotypes. Unsupervised clustering was used to define immune-hot and immune-cold subtypes. A six-gene prognostic signature (PYGL, SFRP1, FGD3, OLR1, DUSP9, and MASP1) was developed using Cox and Least Absolute Shrinkage and Selection Operator (LASSO) regression, which was validated across four cohorts and evaluated for its association with immune infiltration, tumor mutation burden (TMB), and immunotherapy response. The functional role of PYGL was assessed via siRNA knockdown in HNSCC cell lines. RESULTS: The signature demonstrated good performance in stratifying patients into high- and low-risk groups, characterized by distinct survival, immune landscapes, and genomic mutation profiles. High-risk patients showed an immunosuppressive TME, higher TMB, and poorer response to immune checkpoint blockade. PYGL knockdown suppressed proliferation, migration, and clonogenicity. Transcriptomic analysis revealed downregulation of MYC/E2F targets and EMT pathways, alongside concurrent activation of interferon and immune response programs. CONCLUSION: This IRG-based signature offers a clinically translatable tool for prognostication and immunotherapy selection in HNSCC. PYGL represents a key oncogenic driver and potential therapeutic target linking cellular metabolism to tumor progression and immune evasion.
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Multi-omics analysis of macrophage-associated receptor and ligand reveals a strong prognostic signature and subtypes in hepatocellular carcinoma.
Hepatocellular carcinoma (HCC) is a significant contributor to morbidity and mortality worldwide. The interaction between receptors and ligands is the primary mode of intercellular signaling and plays a vital role in the progression of HCC. This study aimed to identify the macrophage-related receptor ligand marker genes associated with HCC and further explored the molecular immune mechanisms attributed to altered biomarkers. Single-cell RNA sequencing data containing primary and recurrent samples were downloaded from the China National GeneBank. Cell types were first identified to explore differences between immune cells from different sample sources. CellChat analysis was used to infer and analyze intercellular communication networks quantitatively. Three molecular subtypes were constructed based on the screened twenty macrophage-associated receptor ligand genes. Bulk RNA-Seq data were downloaded from The Cancer Genome Atlas and Gene Expression Omnibus databases. After the screening, the minor absolute shrinkage and selection operator (LASSO) regression model was employed to identify key markers. After collecting peripheral blood and clinical information from patients, an enzyme-linked immunosorbent assay (ELISA) was used to detect the correlation between key markers and IL-10, one of the macrophage markers. After developing a new HCC risk adjustment model and conducting analysis, it was found that there were significant differences in immune status and gene mutations between the high-risk and low-risk groups of patients based on macrophage-associated receptor and ligand genes. This study identified SPP1, ANGPT2, and NCL as key biological targets for HCC. The drug-gene interaction network analysis identified wortmannin, ribavirin, and tarnafloxin as potential therapeutic drugs for the three key markers. In a clinical cohort study, patients with immune checkpoint inhibitor (ICI) resistance had significantly higher expression levels of OPN, ANGPT2, NCL, and IL-10 than patients with ICI-responsiveness. These three key markers were positively correlated with the expression level of IL-10. The signature based on macrophage-associated receptor and ligand genes can accurately predict the prognosis of patients with HCC and the sensitivity to immunotherapy. These results may help guide the development of targeted prevention and personalized treatment of HCC.
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A Novel Prognostic Signature Integrating Immune and Glycolytic Pathways for Enhanced Prognosis and Immunotherapy Prediction in Hepatocellular Carcinoma.
BACKGROUND: This study aimed to establish an immune-glycolysis-related prognostic signature (IGRPS) to predict hepatocellular carcinoma (HCC) outcomes. Additionally, it explored the role of this signature in the tumor immune microenvironment (TIME), glycolytic pathways, and immunotherapy. METHODS: We analyzed RNA-seq, single-cell sequencing, and immune- and glycolysis-related gene datasets from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO). Using weighted gene co-expression network analysis (WGCNA), F-test, and Cox regression, we identified key survival-related immune and glycolytic genes (SRIGRGs) and developed an IGRPS through multivariate Cox regression. The IGRPS's predictive performance was validated in training and validation cohorts using Kaplan-Meier survival analysis, receiver operating characteristic (ROC) curves, and a prognostic nomogram. Its correlation with TIME and its ability to predict immunotherapy outcomes were also assessed. In vitro experiments were conducted to analyze the expression and function of IGRPS genes in HCC. RESULTS: Thirteen SRIGRGs were identified for constructing the IGRPS. Patients with low-risk scores had significantly longer survival times. The area under the curve (AUC) for ROC curves was over 0.73 for training and 0.7 for validation cohorts, with C-indices of 0.721 and 0.79, respectively. IGRPS was confirmed as an independent prognostic indicator. Patients in the low-risk group showed better responses to combined anti-CTLA4 and anti-PD-1 therapies. In vitro experiments indicated that PRKAG1 and B3GAT3 were upregulated, enhancing glycolysis and promoting HCC cell proliferation and migration. CONCLUSION: The IGRPS, based on immune- and glycolysis-related genes, effectively predicted prognosis and immunotherapy responses in HCC patients.
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Identification of a novel signature for prognostic stratification and integrative analyses in lung adenocarcinoma.
BACKGROUND: Recently, research has revealed that the Golgi apparatus is involved in the development process of cancer; however, the specific effect of Golgi apparatus-related genes (GAGs) in lung adenocarcinoma (LUAD) remains unclear. This study aims to construct a more concise and practical risk model in LUAD using GAG. METHODS: The gene expression profiles of patients with LUAD were downloaded from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases, and GAGs were downloaded from the Gene Set Enrichment Analysis (GSEA) database. Univariate Cox and least absolute shrinkage and selection operator (LASSO) analyses were performed to identify the prognostic GAG signature. Kaplan-Meier and receiver operating characteristic (ROC) curves were plotted to validate the predictive effect of the prognostic signatures. The correlation between the risk model and the immune landscape was examined using CIBERSORT and TIDE analyses. Also, the genes in the signature were assessed by single-cell RNA sequencing (scRNA-seq). RESULTS: A prognostic signature comprising 5 GAG genes (GNPNAT1, RGS20, CAV3, NTSR1, and FURIN) was established after LASSO and multi-Cox analyses. Both the Kaplan-Meier analysis and the ROC curves supported the strong predictive utility of the risk model. Specifically, the former yielded significant stratification in all three validation datasets (P=1.2001e-05, P=0.006, and P=0.04), while the latter provided further evidence of its predictive precision through the area under the curve. In addition, we found that the low-risk group responded better to immunotherapy than the high-risk group (P<0.0001). scRNA-seq analysis revealed the distribution patterns of the 5 GAG genes in cells. Finally, we assessed the situation of tumor mutation burden (TMB) and performed functional analysis based on the risk model of GAGs. CONCLUSIONS: The risk model based on GAGs can effectively stratify the prognosis of patients and predict immunotherapy responses in LUAD.
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Elucidating the molecular and immune interplay between head and neck squamous cell carcinoma and diffuse large B-cell lymphoma through bioinformatics and machine learning.
BACKGROUND: Head and neck squamous cell carcinoma (HNSCC) contributes significantly to global health challenges, presenting primarily in the oral cavity, pharynx, nasopharynx, and larynx. HNSCC has a high propensity for lymphatic metastasis. Diffuse large B-cell lymphoma (DLBCL), the most common subtype of non-Hodgkin lymphoma, exhibits significant heterogeneity and aggressive behavior, leading to high mortality rates. Epstein-Barr virus (EBV) is notably associated with DLBCL and certain types of HNSCC. The purpose of this study is to elucidate the molecular and immune interplay between HNSCC and DLBCL using bioinformatics and machine learning (ML) to identify shared biomarkers and potential therapeutic targets. METHODS: Differentially expressed genes (DEGs) were identified using the "limma" package in R from the HNSCC dataset in The Cancer Genome Atlas (TCGA) database, and relevant modules were selected through weighted gene co-expression network analysis (WGCNA) from a DLBCL dataset in the Gene Expression Omnibus (GEO) database. Based on their intersection genes, functional enrichment analyses were conducted using Gene Ontology (GO), Disease Ontology, and Kyoto Encyclopedia of Genes and Genomes (KEGG) databases. Protein-protein interaction (PPI) networks and ML algorithms were employed to screen for biomarkers. The prognostic value of these biomarkers was evaluated using Kaplan-Meier (K-M) survival analysis and receiver operating characteristic (ROC) curve analyses. The Human Protein Atlas (HPA) database facilitated the examination of messenger RNA (mRNA) and protein expressions. Further analyses of mutations, immune infiltration, drug predictions, and pan-cancer impacts were performed. Additionally, single-cell RNA sequencing (scRNA-seq) data analysis at the cell type level was conducted to provide deeper insights into the tumor microenvironment. RESULTS: From 2,040 DEGs and 1,983 module-related genes, 85 shared genes were identified. PPI analysis with six algorithms proposed 21 prospective genes, followed ML examination yielded 16 candidates. Survival and ROC analyses pinpointed four hub genes-ACACB, MMP8, PAX5, and TNFAIP6-as significantly associated with patient outcomes, demonstrating high predictive capabilities. Evaluations of mutations and immune infiltration, coupled with drug prediction and a comprehensive cancer analysis, highlighted these biomarkers' roles in tumor immune response and treatment efficacy. The scRNA-seq data analysis revealed an increased abundance of fibroblasts, epithelial cells and mononuclear phagocyte system (MPs) in HNSCC tissues compared to lymphoid tissues. MMP8 showed higher expression in five cell types in HNSCC tissues, while TNFAIP6 and PAX5 exhibited higher expression in specific cell types. CONCLUSIONS: Leveraging bioinformatics and ML, this study identified four pivotal genes with significant diagnostic capabilities for DLBCL and HNSCC. The survival analysis corroborates their diagnostic accuracy, supporting the development of a diagnostic nomogram to assist in clinical decision-making.