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[Exploring the Role of PCDHGB4 in the Occurrence of Lung Squamous Cell Carcinoma Based on Bioinformatics Analysis].
BACKGROUND: Lung squamous cell carcinoma (LUSC) is a subtypes of non-small cell lung cancer (NSCLC). It has been reported that members of the protocadherin γ family can regulate tumor cell growth by inhibiting the Wnt signaling pathway. Protocadherin-gamma subfamily B4 (PCDHGB4) as a family member in LUSC was rarely reported. The aim of this study was to investigate the role and potential prognostic value of PCDHGB4 in the development of LUSC using bioinformatics methods. METHODS: The Cancer Genome Atlas (TCGA), cBioPortal and UALCAN databases were used to analyze the expression, prognosis, clinicopathological features, immune cell infiltration, immune regulatory genes, immune checkpoint inhibitors (ICIs), and methyltransferases of PCDHGB4 in LUSC. At the single cell level, we analyzed the clustering results of cell subtypes and the expression of PCDHGB4 in different immune cell subpopulations. In addition, we compared the promoter methylation levels of PCDHGB4 in LUSC tissues and normal tissues and performed protein-protein interaction and mutation analysis. Finally, enrichment analysis was performed based on the differentially expressed genes. RESULTS: Bioinformatics analysis results showed that the expression level of PCDHGB4 in LUSC tissues was lower than that in normal tissues. Survival analysis showed that increased PCDHGB4 expression was associated with poor prognosis. Single-cell sequencing analysis showed that PCDHGB4 was expressed in T cells, monocytes or macrophages, and dendritic cells. It was further found that PCDHGB4 played an important role in tumor immunity and confirmed that PCDHGB4 was associated with immune checkpoints, immune regulatory genes, and methyltransferases. Besides, enrichment analysis revealed that PCDHGB4 was involved in multiple cancer-related pathways. CONCLUSIONS: The expression of PCDHGB4 was low in LUSC. PCDHGB4 was related to the poor prognosis of patients, and PCDHGB4 was closely related to the infiltration and pathway of tumor immune cells. PCDHGB4 may be a potential prognostic marker and a new target for immunotherapy in LUSC.
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Single-cell landscape of human adipose tissue reveals THBS1 as a potential driver of fibro-inflammation in metabolic disease.
The cellular complexity of adipose tissue plays a critical role in the pathogenesis of metabolic disease; however, mechanisms underlying adipose dysfunction remain poorly understood. Here, we constructed a single-nucleus and bulk transcriptomic atlas of human visceral adipose tissues from an ethnically homogeneous cohort with defined metabolic states: lean with normal glucose tolerance, obesity, and obesity with type 2 diabetes. Single-nucleus RNA sequencing (snRNA-seq) revealed a fibro-inflammatory subpopulation of adipose stem cells and adipocytes characterized by high THBS1 expression, which were enriched in individuals with obesity and obesity with type 2 diabetes compared to lean controls. Through complementary in vitro (human primary ASCs, THP-1 macrophages, and Matrigel-captured mature adipocytes), ex vivo (mouse and human visceral adipose tissue explants), and in vivo (gain-of-function and antibody-mediated neutralization) experiments, we demonstrated that THBS1 promoted adipose fibrosis and inflammation via macrophage-mediated TGFβ activation, impairing adipogenesis and adipocyte function. Inhibition of THBS1 attenuated fibro-inflammatory features in metabolically dysfunctional adipose tissues. Moreover, THBS1 levels in plasma and adipocytes correlated with metabolic traits, including BMI and HbA1c. Collectively, our study provides a cellular atlas of human visceral adipose tissues across metabolic disease states and identifies THBS1 as a key pathogenic driver and potential therapeutic target.
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Mapping the Single-Cell Differentiation Landscape of Osteosarcoma.
PURPOSE: The genetic intratumoral heterogeneity observed in human osteosarcomas poses challenges for drug development and the study of cell fate, plasticity, and differentiation, which are processes linked to tumor grade, cell metastasis, and survival. EXPERIMENTAL DESIGN: To pinpoint errors in osteosarcoma differentiation, we transcriptionally profiled 31,527 cells from a tissue-engineered model that directs mesenchymal stem cells toward adipogenic and osteoblastic fates. Incorporating preexisting chondrocyte data, we applied trajectory analysis and non-negative matrix factorization to generate the first human mesenchymal differentiation atlas. RESULTS: This "roadmap" served as a reference to delineate the cellular composition of morphologically complex osteosarcoma tumors and quantify each cell's lineage commitment. Projecting a bulk RNA-sequencing osteosarcoma dataset onto this roadmap unveiled a correlation between a stem-like transcriptomic phenotype and poorer survival outcomes. CONCLUSIONS: Our study quantifies osteosarcoma differentiation and lineage, a prerequisite to better understanding lineage-specific differentiation bottlenecks that might someday be targeted therapeutically.
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Identification of the novel therapeutic target gene SLC12A9, which determines the prognosis of patients with colorectal cancer.
BACKGROUND: Feasible therapeutic targets need to be identified to overcome tumor heterogeneity and ameliorate therapeutic resistance. We explored the identification of oncogenes on Ch.7q, which is amplified ubiquitously in colorectal cancer (CRC). In this study, we focused on SLC12A9, which encodes a channel protein that regulates ion concentration inside and outside of the cell. MATERIALS AND METHODS: Expression and survival analyses of SLC12A9 were performed using reverse transcription quantitative polymerase chain reaction, The Cancer Genome Atlas dataset, and immunohistochemistry. A gene set enrichment analysis (GSEA) was performed to elucidate the correlation between SLC12A9 and gene sets associated with tumor progression. Subsequently, an in vitro proliferation assay was performed using SLC12A9-knockdown CRC cells. RESULTS: SLC12A9 was highly expressed in tumor cells, and its high expression was associated with a poor prognosis. The GSEA revealed an association with mTORC1 signaling. In the colony formation assay, SLC12A9 knockdown by siRNA suppressed the proliferative capacity of CRC cells. Public single-cell RNA sequencing data have revealed that SLC12A9 is derived from malignant epithelial cells in CRC tissues. CONCLUSION: Our results suggest that abundant SLC12A9 expression is associated with a poor prognosis due to the devastating growth of CRC cells, in part via the mTORC1 signaling pathway.
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Pan-cancer analysis: predictive role of TAP1 in cancer prognosis and response to immunotherapy.
BACKGROUND: Transporter associated with antigen processing 1 (TAP1) is a molecule involved in processing and presentation of major histocompatibility complex class I restricted antigens, including tumor-associated antigens. TAP1 participates in tumor immunity, and is aberrantly expressed in multiple cancer types; METHODS: Transcriptome profiles were obtained from The Cancer Genome Atlas and Genotype-Tissue Expression databases. Genetic alterations, protein distribution, and interaction information for TAP1 were downloaded from cBioPortal, Human Protein Atlas and Compartmentalized Protein-Protein Interaction, respectively. Single-cell analyses of TAP1 across cancers were conducted via the Tumor Immune Single-cell Hub website. Gene set enrichment analysis was employed to investigate TAP1-associated functional mechanisms and processes. Immune cell infiltration was explored using Tumor Immune Estimation Resource 2.0. Pan-cancer correlations between TAP1 expression and immunotherapy biomarkers were explored using the Spearman's correlation test. Associations with immunotherapy responses were also investigated using clinicopathological and prognostic information from cohorts of patients with cancer receiving immune checkpoint inhibitors. RESULTS: TAP1 expression was elevated in most cancer types and exhibited distinct prognostic value. Immune cells expressed more TAP1 than malignant cells within most tumors. TAP1 expression was significantly correlated with immune-related pathways, T-lymphocyte infiltration, and immunotherapeutic biomarkers. Clinical cohort validation revealed a significant correlation with immune therapeutic effects and verified the prognostic role of TAP1 in immunotherapy. Western blot assay indicated that TAP1 is upregulated in glioblastoma compared with adjacent normal brain tissues. CONCLUSION: TAP1 is a robust tumor prognostic biomarker and a novel predictor of clinical prognosis and immunotherapeutic responses in various cancer types.
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Transcriptomic analysis reveals the potential biological mechanism of AIS and lung adenocarcinoma.
INTRODUCTION: Acute ischemic stroke (AIS) and lung adenocarcinoma (LUAD) are associated with some of the highest morbidity and mortality rates worldwide. Despite reports on their strong correlation, the causal relationship is not fully understood. The study aimed to identify and annotate the biological functions of hub genes with clinical diagnostic efficacy in AIS and LUAD. METHODS: Transcriptome and single-cell datasets were obtained from the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA). We identified the differentially expressed genes (DEGs) upregulated in AIS and LUAD and found 372 genes intersecting both datasets. Hub genes were identified using protein-protein interaction (PPI) networks, and the diagnostic and prognostic utility of these hub genes was then investigated using receiver operating characteristic (ROC) curves, survival analysis, and univariable Cox proportional hazard regression. Single-cell analysis was used to detect whether the hub genes were expressed in tumor epithelial cells. The immune microenvironment of AIS and LUAD was assessed using the CIBERSORT algorithm. The protein expression of these hub genes was tracked using the Human Protein Atlas (HPA). We calculated the number of positive cells using the digital pathology software QuPath. Finally, we performed molecular docking after using the Enrichr database to predict possible medicines. RESULTS: We identified the molecular mechanisms underlying hub genes in AIS and LUAD and found that CCNA2, CCNB1, CDKN2A, and CDK1 were highly expressed in AIS and LUAD tissue samples compared to controls. The hub genes were mainly involved in the following pathways: the cell cycle, cellular senescence, and the HIF-1 signaling pathway. Using immunohistochemical slices from the HPA database, we confirmed that these hub genes have a high diagnostic capability for AIS and LUAD. Further, their high expression is associated with poor prognosis. Finally, curcumin was tested as a potential medication using molecular docking modeling. DISCUSSION: Our findings suggest that the hub genes we found in this study contribute to the development and progression of AIS and LUAD by altering the cellular senescence pathway. Thus, they may be promising markers for diagnosis and prognosis.
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An Eight-Gene Metabolic Prognostic Signature in Head and Neck Squamous Cell Carcinoma: Cross-Cohort Evaluation, Single-Cell Analysis, and Preliminary Functional Evaluation of PYGL and G6PD.
BACKGROUND: Head and neck squamous cell carcinoma (HNSCC) is the most prevalent pathological subtype of head and neck malignancies. Although metabolic reprogramming contributes to malignant progression and adaptation within the tumor microenvironment in HNSCC, its biological and prognostic implications remain incompletely understood. OBJECTIVE: To develop and evaluate a metabolism-related prognostic score for head and neck squamous cell carcinoma (HNSCC) across multiple cohorts, identify its cellular sources at single-cell resolution, and perform preliminary in vitro functional evaluation of PYGL and G6PD. METHODS: Metabolism-related differentially expressed genes were screened in The Cancer Genome Atlas Head and Neck Squamous Cell Carcinoma cohort (TCGA-HNSC). A total of 506 patients with survival data were divided into a training set (n = 355) and an internal test set (n = 151) at a 7:3 ratio. Training-only repeated nested cross-validation benchmarked 126 algorithmic configurations. Survival-SVM had the highest mean out-of-fold C-index, whereas an eight-gene LASSO + StepCox model was retained for interpretability and cross-cohort transportability. External cohort evaluation of overall survival (OS) was performed in GSE65858 (n = 270), while GSE117973 (n = 77), which used disease-specific survival (DSS), was treated as an exploratory cohort. By integrating 54,163 single cells from the GSE150321 and GSE172577 datasets, analyses, including inferCNV, 12 predefined metabolic modules, CellChat, and scTenifoldKnk, were conducted to investigate the cellular origins of the risk score and candidate regulatory networks; additionally, PYGL- and G6PD-targeting siRNAs were evaluated for their effects on target protein expression and wound closure in vitro. RESULTS: We developed a metabolic prognostic model that included HPRT1, ADA, PYGL, P4HA1, LDHA, G6PD, NT5E, and FTH1. The concordance indices (C-indices) were 0.638 in the TCGA training set, 0.651 in the internal test set, and 0.604 in GSE65858. In a complete-case clinical cohort of 473 TCGA patients, a model combining the risk score with age, sex, and T, N, and M categories achieved C-indices of 0.693 in TCGA and 0.677 in GSE65858. The corresponding 3-year areas under the receiver operating characteristic curve (AUCs) were 0.735 and 0.700. Single-cell analysis showed that the eight genes were distributed across malignant epithelial, proliferating, immune, endothelial, and stromal populations. The 16,240 copy number variation-high (CNV-high) epithelial cells had higher eight-gene scores and higher transcriptional scores for glycogen metabolism, the pentose phosphate pathway, glycolysis, and redox-related programs. Small interfering RNA (siRNA)-mediated silencing of G6PD or PYGL reduced relative wound closure at 12 h in TU686 and SCC25 cells. CONCLUSION: The eight-gene metabolic score provided moderate and cohort-dependent prognostic discrimination in the TCGA internal test set and GSE65858. Its bulk transcriptomic signal arose from several cell populations. Silencing PYGL or G6PD reduced target protein expression and wound closure. These findings provide preliminary in vitro evidence linking both genes to a migratory phenotype in HNSCC.
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Mitochondrial single-stranded DNA binding protein 1 (SSBP1) high expression as a potential biomarker and association with poor prognosis in hepatocellular carcinoma (HCC).
BACKGROUND: Single-stranded DNA binding protein 1 (SSBP1) is a DNA binding protein found in mitochondria, encoded by nuclear genes. SSBP1 plays a crucial role in responding to mitochondrial DNA (mtDNA) damage and maintaining genome stability, and it is linked to cancer occurrence and progression, but its role in hepatocellular carcinoma (HCC) is still unclear. Therefore, the aim of this research was to investigate the expression of SSBP1 and its potential clinical significance in HCC. METHODS: RNA-seq data and clinical information of HCC samples and normal liver samples were downloaded from The Cancer Genome Atlas (TCGA). The expression of SSBP1 in HCC and its correlation with clinical pathological indicators, prognosis, immune cells, and infiltration were analyzed using R software, while the diagnostic value of SSBP1 in HCC was evaluated. Using the R software, we conducted Gene Ontology (GO) analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis, and gene set enrichment analysis (GSEA) on the SSBP1 expression in HCC. Immunohistochemistry (IHC) detected SSBP1 expression in 31 HCC tissue pairs. Western blotting and quantitative reverse transcription polymerase chain reaction (qRT-PCR) quantified protein and mRNA levels in 6 fresh HCC tissue. Protein expression and distribution of SSBP1 in HCC cell lines were analyzed using qRT-PCR, Western blotting, and Immunofluorescence techniques. RESULTS: SSBP1 mRNA expression was significantly higher in HCC tissues compared to normal tissues (P<0.001) in both matched and unmatched samples. SSBP1 expression was correlated with gender and M stage (P<0.05), but not with other factors. High SSBP1 expression was identified as an independent risk factor for overall survival (OS) in HCC patients [hazard ratio =1.713, P=0.01]. Immunocell infiltration analysis showed a negative correlation between SSBP1 expression and level of naive B cells, but a positive correlation with memory B cells and macrophages (|Spearman's r| >0.2, P<0.05). The diagnostic value of SSBP1 mRNA expression for early diagnosis prognosis of HCC (area under the curve >0.50). The GO enrichment analysis of SSBP1 revealed that it was enriched for mitochondrial biological functions. KEGG analysis showed that SSBP1 was associated with multiple DNA replication, mismatch repair and homologous recombination pathways. GSEA analysis showed that the first three pathways strongly related to the high expression of SSBP1 were DNA repair, myc-targets-v1 and reactive oxygen species signaling pathways. Validation through IHC and Western blotting high SSBP1 protein expression in HCC tissue, as well as qRT-PCR and Western blotting results showed high expression in HCC cell lines. Immunofluorescence experiments indicated the localization of SSBP1 in the mitochondria of HCC cells. CONCLUSIONS: High expression of SSBP1 is an independent risk factor for poor prognosis in HCC patients and has good diagnostic value.
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Tumor type classification and candidate cancer-specific biomarkers discovery via semi-supervised learning.
Identifying cancer-related differentially expressed genes provides significant information for diagnosing tumors, predicting prognoses, and effective treatments. Recently, deep learning methods have been used to perform gene differential expression analysis using microarray-based high-throughput gene profiling and have achieved good results. In this study, we proposed a new robust multiple-datasets-based semi-supervised learning model, MSSL, to perform tumor type classification and candidate cancer-specific biomarkers discovery across multiple tumor types and multiple datasets, which addressed the following long-lasting obstacles: (1) the data volume of the existing single dataset is not enough to fully exert the advantages of deep learning; (2) a large number of datasets from different research institutions cannot be effectively used due to inconsistent internal variances and low quality; (3) relatively uncommon cancers have limited effects on deep learning methods. In our article, we applied MSSL to The Cancer Genome Atlas (TCGA) and the Gene Expression Comprehensive Database (GEO) pan-cancer normalized-level3 RNA-seq data and got 97.6% final classification accuracy, which had a significant performance leap compared with previous approaches. Finally, we got the ranking of the importance of the corresponding genes for each cancer type based on classification results and validated that the top genes selected in this way were biologically meaningful for corresponding tumors and some of them had been used as biomarkers, which showed the efficacy of our method.
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Single-Cell RNA-Sequencing Shift in the Interaction Pattern Between Glioma Stem Cells and Immune Cells During Tumorigenesis.
Glioblastoma is one of the most common neoplasms in the central nervous system characterized by limited immune response and unlimited expansion capability. Cancer stem cells (GSCs), a small fraction of the tumor cells, possess a pivotal regulation capability in the tumor microenvironment with a superior proliferation ability. We aimed to reveal the interaction between glioma stem cells (GSCs) and immune cells during tumorigenesis. Single-cell sequencing data from seven surgical specimens of glioblastoma patients and patient-derived GSCs cocultured with peripheral leukocytes were used for the analysis. Cell grouping and trajectory analysis were performed using Seurat and Monocle 3 packages in R software. The gene set of Cancer Genome Anatomy Project was used to define different cell types. Cells with the ability of proliferation and differentiation in glioblastoma tissue were defined as GSCs, which had a similar expression pattern to that in the GSCs in vitro. Astrocytes in glioblastoma were mainly derived from differentiated GSCs, while oligodendrocytes were most likely to be derived from different precursor cells. No remarkable evolutionary trajectory was observed among the subgroups of T cells in glioblastoma. The immune checkpoint interaction between GSCs and immune cells was changed from stimulatory to inhibitory during tumorigenesis. The patient-derived GSCs system is an ideal model for GSC research. The above research revealed that the interaction pattern between GSC glioma stem cells and immune cells during tumorigenesis provides a theoretical basis for GSC glioma stem cell-targeted immunotherapy.