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Identification of Genes and Construction of Prognostic Model of Lung Adenocarcinoma Based on Propionate Metabolism-Related Genes.
BACKGROUND: Dysregulation of propionate metabolism can enhance the invasive properties of lung adenocarcinoma (LUAD) cells and increase their metastatic potential. Therefore, we constructed a predictive model based on propionate metabolism-related genes (PMRGs) to evaluate the prognosis of patients with LUAD. METHODS: mRNA expression profiles and clinical data of LUAD patients were obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO). The predictive model was constructed using least absolute shrinkage and selection operator (LASSO). The associations between the risk score and tumor prognosis, immune infiltration, drug sensitivity, signaling pathways, and clinical features were evaluated using the CIBERSORT algorithm, the Genomics of Drug Sensitivity in Cancer (GDSC) database, gene set variation analysis (GSVA), gene set enrichment analysis (GSEA), and nomogram analyses. For hub genes, motif enrichment, genome-wide association study (GWAS) analysis and single-cell analysis were performed. Expression differences between LUAD cells and normal lung epithelial cells were validated using quantitative real-time polymerase chain reaction (qRT-PCR). RESULTS: We established a LUAD prognostic model containing 16 hub genes. The area under the receiver operating characteristic curve (AUC) values for the model were 0.722 (1-year overall survival (OS)), 0.696 (3-year OS), and 0.700 (5-year OS). GSVA revealed significant enrichment in the G2/M checkpoint, E2F targets, and glycolysis pathways. GSEA indicated enrichment of the hypoxia-inducible factor-1 (HIF-1), interleukin (IL)-17, and p53 signaling pathways. Transcription factor analysis identified the motif cisbp-M4287 as the most significantly enriched, with a normalized enrichment score (NES) of 5.9. qRT-PCR results showed that the expression of ADIPOQ, CYP27A1, and GCDH was downregulated, while the expression of SERPINE1 was upregulated. No statistically significant differences were observed in the expression of CYP17A1 and EHHADH. CONCLUSIONS: A novel prognostic model based on PMRGs was established to predict overall survival in LUAD patients.
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Single-Cell Characterization of the Immune Microenvironment of Melanoma Brain and Leptomeningeal Metastases.
PURPOSE: Melanoma brain metastases (MBM) and leptomeningeal melanoma metastases (LMM) are two different manifestations of melanoma CNS metastasis. Here, we used single-cell RNA sequencing (scRNA-seq) to define the immune landscape of MBM, LMM, and melanoma skin metastases. EXPERIMENTAL DESIGN: scRNA-seq was undertaken on 43 patient specimens, including 8 skin metastases, 14 MBM, and 19 serial LMM specimens. Detailed cell type curation was performed, the immune landscapes were mapped, and key results were validated by IHC and flow cytometry. Association analyses were undertaken to identify immune cell subsets correlated with overall survival. RESULTS: The LMM microenvironment was characterized by an immune-suppressed T-cell landscape distinct from that of brain and skin metastases. An LMM patient with long-term survival demonstrated an immune repertoire distinct from that of poor survivors and more similar to normal cerebrospinal fluid (CSF). Upon response to PD-1 therapy, this extreme responder showed increased levels of T cells and dendritic cells in their CSF, whereas poor survivors showed little improvement in their T-cell responses. In MBM patients, therapy led to increased immune infiltrate, with similar T-cell transcriptional diversity noted between skin metastases and MBM. A correlation analysis across the entire immune landscape identified the presence of a rare population of dendritic cells (DC3) that was associated with increased overall survival and positively regulated the immune environment through modulation of activated T cells and MHC expression. CONCLUSIONS: Our study provides the first atlas of two distinct sites of melanoma CNS metastases and defines the immune cell landscape that underlies the biology of this devastating disease.
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AMPKα2 promotes tumor immune escape by inducing CD8+ T-cell exhaustion and CD4+ Treg cell formation in liver hepatocellular carcinoma.
BACKGROUND: Adenosine monophosphate-activated protein kinase (AMPK) is associated with the development of liver hepatocellular carcinoma (LIHC). AMPKα2, an α2 subunit of AMPK, is encoded by PRKAA2, and functions as the catalytic core of AMPK. However, the role of AMPKα2 in the LIHC tumor immune environment is unclear. METHODS: RNA-seq data were obtained from the Cancer Genome Atlas and Genotype-Tissue Expression databases. Using the single-cell RNA-sequencing dataset for LIHC obtained from the China National Genebank Database, the communication between malignant cells and T cells in response to different PRKAA2 expression patterns was evaluated. In addition, the association between PRKAA2 expression and T-cell evolution during tumor progression was explored using Pseudotime analysis, and the role of PRKAA2 in metabolic reprogramming was explored using the R "scMetabolis" package. Functional experiments were performed in LIHC HepG2 cells. RESULTS: AMPK subunits were expressed in tissue-specific and substrate-specific patterns. PRKAA2 was highly expressed in LIHC tissues and was associated with poor patient prognosis. Tumors with high PRKAA2 expression displayed an immune cold phenotype. High PRKAA2 expression significantly promoted LIHC immune escape. This result is supported by the following evidence: 1) the inhibition of major histocompatibility complex class I (MHC-I) expression through the regulation of interferon-gamma activity in malignant cells; 2) the promotion of CD8+ T-cell exhaustion and the formation of CD4+ Treg cells in T cells; 3) altered interactions between malignant cells and T cells in the tumor immune environment; and 4) induction of metabolic reprogramming in malignant cells. CONCLUSIONS: Our study indicate that PRKAA2 may contribute to LIHC progression by promoting metabolic reprogramming and tumor immune escape through theoretical analysis, which offers a theoretical foundation for developing PRKAA2-based strategies for personalized LIHC treatment.
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Pan-Cancer Analysis Reveals AEBP1-Collagen Co-Expression and Its Potential Role in CAF-Mediated Tumor Stiffness.
Cancer-associated fibroblasts (CAFs) are critical components of the tumor microenvironment that promote cancer progression and immune evasion. Adipocyte enhancer-binding protein 1 gene (AEBP1), which encodes aortic carboxypeptidase-like protein (ACLP), has been implicated in tissue remodeling and fibrosis, yet its role in CAF biology across cancers remains poorly understood. Here, we performed a pan-cancer transcriptomic analysis using The Cancer Genome Atlas (TCGA) and found that AEBP1 expression strongly correlates with expression of collagen family genes in the majority of solid tumors. Integration of single-cell RNA-sequencing datasets from breast and pancreatic cancers revealed that AEBP1 is predominantly expressed in CAFs, where it is co-expressed with collagens and CAF marker genes. Functional experiments using three-dimensional (3D) spheroids composed of oral squamous cell carcinoma (OSCC)-derived CAFs showed that AEBP1 knockdown significantly reduced spheroid stiffness without altering their morphology or size, indicating that ACLP contributes to the mechanical properties of tumor tissues. Together with earlier findings linking AEBP1/ACLP to reduced CD8+ T-cell infiltration, our results suggest that stromal AEBP1/ACLP enhances both extracellular matrix stiffness and immune suppression and highlights AEBP1/ACLP as a potential therapeutic target through which to remodel the tumor microenvironment and improve anti-tumor immunity.
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Establishment of a Prognostic Model for Pancreatic Cancer Based on Hypoxia-Related Genes.
OBJECTIVES: Pancreatic cancer presents a formidable challenge with its aggressive nature and dismal prognosis, often hampered by elusive early symptoms. The tumor microenvironment (TME) emerges as a pivotal player in pancreatic cancer progression and treatment responses, characterized notably by hypoxia and immunosuppression. In this study, we aimed to identify hypoxia-related genes and develop a prognostic model for pancreatic cancer leveraging these genes. METHODS: Through analysis of gene expression data from The Cancer Genome Atlas (TCGA) and subsequent GO/KEGG enrichment analysis, hypoxia-related pathways were identified. We constructed a prognostic model using lasso regression and validated it using an independent dataset. RESULTS: Our results showed that expression levels of PLAU, SLC2A1, and CA9 exhibited significant associations with prognosis in pancreatic cancer. The prognostic model, built upon these genes, displayed robust predictive accuracy and was validated in an independent dataset. Furthermore, we found a correlation between the risk score of the prognostic model and clinical parameters of pancreatic cancer patients. At the same time, we also explored the relationship between the established hypoxia-related prognostic model and the immune microenvironment at the single-cell level. RT-qPCR results showed notable differences in the expression of hypoxia pathway-related genes between normal PANC-1 and hypoxic-treated PANC-1 cells. CONCLUSION: Our study provides insights into the role of the hypoxic microenvironment in pancreatic cancer and offers a promising prognostic tool for clinical application.
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Spatiotemporal Transcriptomic Profiling Reveals the Dynamic Immunological Landscape of Alveolar Echinococcosis.
Alveolar echinococcosis (AE) is caused by the chronic infection of E. multilocularis, whose tumor-like growth can lead to high fatality if improperly treated. The early diagnosis of infection and the treatment of advanced AE remain challenging. Herein, bulk RNA-seq, scRNA-seq, and spatial transcriptomics technologies are integrated, to reveal the host immune response mechanism against E. multilocularis both spatially and chronologically, collecting mouse liver samples at multiple timepoints up to 15 months post infection. These results unveil an unprecedented high-resolution spatial atlas of the E. multilocularis infection foci and the functional roles of neutrophils, Spp1+ macrophages, and fibroblasts during disease progression. The heterogeneity of neutrophil and macrophage subpopulations are critical in both parasite-killing and the occurrence of immunosuppression during AE progression. These findings indicate the transition of parasite control strategy from "active killing" to "negative segregation" by the host, providing instructive insights into the treatment strategy for echinococcosis.
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Bioinformatics Analysis Combined With Experiments Predicts PUDP as a Potential Prognostic Biomarker for Hepatocellular Carcinoma Through Its Interaction With Tumor Microenvironment.
Hepatocellular carcinoma (HCC) is one of the deadliest tumors in the world and is notorious for poor prognosis. There is mounting evidence that pseudouridine performs key functions in the initiation and progression of several cancers. A previous study demonstrated that Pseudouridine 5'-phosphatase (PUDP) may be a novel prognostic biomarker in colorectal cancer. However, in the past, we have paid little attention to PUDP and we are still not clear about its function and role in cancer. In this study, a pan-cancer analysis of PUDP expression and prognosis was performed firstly using The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx) data and we found that PUDP may be a potential oncogene for HCC. Then the most potential upstream microRNA contributing to PUDP was identified as let-7c-5p through expression analysis, correlation analysis, and survival analysis. Subsequently, the result of single cell RNA sequencing (scRNA-seq) demonstrated that PUDP was significantly highly expressed on malignant cells. In addition, there are significantly positive correlations between PUDP and tumor immune cell infiltration, biomarkers of immune cells, and immune checkpoint expression, especially with tumor-promoting immune cells such as T cell regulatory (Treg), Myeloid-derived suppressor cell (MDSC), cancer-associated fibroblast (CAF). Moreover, we found the methylation level of three loci was positively correlated with PUDP expression and four loci were negatively correlated. 15 pairs of HCC and normal adjacent tissues from HCC patients who were treated at our center were used to verify the results of the bioinformatics analysis and the results of experiments are similar to the bioinformatics analysis. Our study demonstrated that HCC patients with high PUDP expression are less likely to benefit from immunotherapy, and in addition, we explored the relationship between PUDP and anticancer drugs. Finally, we explored the clinical relevance of PUDP, identified PUDP as an independent risk factor for HCC patients and constructed a prognostic model, used International Cancer Genome Consortium (ICGC) data to do external validation. Collectively, our study demonstrated that high expression of PUDP suggested a poor prognosis and low response to immunotherapy, providing new insight into the treatment and prognosis of HCC.
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The role of hypoxia-senescence co-related molecular subtypes and prognostic characteristics in hepatocellular carcinoma.
Hepatocellular carcinoma (HCC) is known for its high invasiveness, high fatality rate. Both hypoxia and senescence play crucial roles in the initiation and progression of cancer, yet their prognostic implications in HCC are yet to be fully understood. The hypoxia-senescence co-related genes (HSCRGs) were screened from public databases. Transcriptome data and clinical information were obtained from patients with HCC using the Cancer Genome Atlas, GSE76427, and International Cancer Genome Consortium (ICGC). The random forest tree algorithm was used to identify the characteristic genes of the disease, and the genes were verified by related experiments. SVM algorithm was used to classify HCC patients based on HSCRGs. The prediction model based on HSCRGs was established by LASSO, univariate and multivariate COX regression analysis. We used the ICGC for outside validation. The risk score model was analyzed from subgroup analysis, immune infiltration, and functional strength. The expression patterns of key prognostic genes in tumor microenvironment were decoded by single cell analysis. A total of 184 HSCRGs were identified. The expression pattern and functional characteristics of MLH1 gene in HCC were verified. Two HCC subtypes were identified based on HSCRGs. Then, a prediction model based on HSCRGs was established, and risk score was identified as an independent prognostic indicator of HCC. A new nomogram is constructed and shows good prediction ability. We further determined that the level of infiltration of immune cells and the expression of immune checkpoints are significantly affected by the risk score. The immune microenvironment was different between the two risk groups. The high-risk group was dominated by immunosuppressed cells, and the prognosis was poor. Single-cell analysis revealed the expression of seven key prognostic genes in the tumor microenvironment. Finally, qPCR results further verified the expression levels of seven prognostic genes. HSCRGs are of great significance in the prognosis prediction, risk stratification and targeted therapy of patients with HCC.
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Selenium metabolism heterogeneity in pan-cancer: insights from bulk and single-cell RNA sequencing.
BACKGROUND: Selenium, a natural microelement with both nutritional and toxicological properties, is intertwined with tumorigenesis and progression. However, it is not fully understood how selenium metabolism affects immune response and cancer biology. METHODS: We estimated selenium metabolism by Gene Set Enrichment Analysis (GSEA) to delineate the selenium metabolism landscape using The Cancer Genome Atlas (TCGA), Genotype-Tissue Expression (GTEx), Cancer Cell Line Encyclopedia (CCLE) and a integrated pan-cancer single-cell dataset. We systematically explored the prognostic implications of selenium metabolism and selenium-related regulatory patterns. The therapeutic value of selenium metabolism was explored through machine learning and examined in several immunotherapy cohorts. The heterogeneity and underlying mechanism of selenium metabolism were investigated by cell‒cell communication analysis at the single-cell level. RESULTS: A GSEA analysis based on 86 genes was used to evaluate the selenium metabolism landscape. The selenium metabolism score exhibited prognostic value in predicting the lower risk of mortality, possibly due to its correlation with multiple cancer hallmarks, including a positive correlation with complement (R = 0.761, P < 0.001), inflammatory response (R = 0.663, P < 0.001), apoptosis (R = 0.626, P < 0.001), hypoxia (R = 0.587, P < 0.001), reactive oxygen species (ROS) (R = 0.558, P < 0.001), and interferon gamma response (R = 0.539, P < 0.001). We also observed heterogeneity in the relationship between selenium metabolism and immunity across different cancers. Based on selenium-related genes, we constructed a machine learning model with area under the ROC curve (AUC) of 0.82 in predicting immune checkpoint inhibitor (ICI)-based immunotherapy response. Single-cell selenium metabolism quantification revealed that adjacent and tumor tissues had higher selenium metabolism compared with normal tissues, especially in epithelial cells, fibroblasts and macrophages. The communication between high-selenium epithelium and high-selenium fibroblast was significantly higher than other cells, especially in cytokines, chemokines, collagen, Wnt, VEGF, IGF and FGF pathways. CONCLUSION: Our study provides a comprehensive landscape of selenium metabolism levels and diverse regulatory patterns in different cancers, deepening the understanding of selenium's roles in tumorigenesis and immunity.
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NIFK, a Potential Prognostic and Immunological Biomarker in Pan-Cancer Analysis, Significantly Regulates Proliferation and Metastasis of Colorectal Cancer.
BACKGROUND: As a binding protein of Ki67, NIFK plays an important role in the mitosis of cells and is closely related to the progression of specific types of tumors. However, there is still a lack of systematic analysis of NIFK in pan-cancer and insufficient research to explore its role in human tumors. METHODS: We systematically evaluated the pan-cancer expression and mutation of NIFK in human cancers using data from The Cancer Genome Atlas (TCGA) through large-scale bioinformatics analysis. In addition, we explored the pan-cancer immunological characteristics of NIFK, especially in colorectal adenocarcinoma (COAD). Furthermore, we used single-cell sequencing to analyze the expression of NIFK in different cells of COAD tissues and performed GO, KEGG, and gene set enrichment analysis of NIFK in COAD. Lastly, we evaluated the effects of NIFK knockdown on the colorectal cancer cell lines in in vitro experiment. RESULTS: We found that NIFK was overexpressed in almost all types of tumors and showed significant prognostic efficacy. Additionally, correlations between NIFK and specific immune features, such as immune cell infiltration, immune checkpoint genes, TMB, and MSI, suggest that NIFK may be used to guide immunotherapy. Subsequently, it was found that the expression of NIFK was significantly upregulated in tumor cells through single-cell sequencing analysis, and the NIFK gene was closely associated with tumor progression and immune therapy response. Finally, we further elucidated the role of NIFK in colorectal cancer and found that downregulation of NIFK expression could inhibit the proliferation, migration, and invasion ability of colorectal cancer cells. CONCLUSION: The results of this study demonstrated that NIFK, as a member of the pan-cancer genes, will serve as a biomarker and a potential therapeutic target for a range of cancer types, providing new insight into precision medicine.