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Association of CDKN2A overexpression with poor prognosis, radioresistance, and immunosuppression in colorectal cancer.
BACKGROUND: In colorectal cancer (CRC), dysregulated cyclin-dependent kinase inhibitor 2A (CDKN2A) contributes to disease progression. This investigation sought to explore the link between CDKN2A expression and the effectiveness of radiotherapy in CRC, and to investigate the underlying mechanisms. METHODS: Transcriptomic and clinical datasets were procured from the Cancer Genome Atlas and Gene Expression Omnibus databases. Immune cell infiltration was assessed using xCell, CIBERSORT, and EPIC methods. Biological mechanisms were investigated through the utilization of Gene Ontology and Kyoto Encyclopedia of Genes and Genomes. HCT 116 cells were used for in vitro and in vivo experiments. RESULTS: Bulk and single-cell transcriptome analyses and immunohistochemistry consistently revealed elevated CDKN2A expression in tumor specimens from patients with CRC. Patients with tumors that were nonresponsive to neoadjuvant chemoradiotherapy had significantly higher CDKN2A expression than those of responders (P = 0.039). CDKN2A knockdown in tumor led to a marked decrease in the proliferation, invasion, and migration capabilities, and this effect was enhanced by combined X-ray irradiation. Elevated CDKN2A expression was linked to markedly worse survival. In the tumor immune microenvironment, CDKN2A expression was negatively linked to CD8+ cell infiltration and positively correlated with the levels of immunosuppressive factors. Functional enrichment analysis indicated that elevated CDKN2A expression is linked to transcriptional regulation, amino acid metabolism, protein folding, and the TGF-β signaling pathway. CONCLUSION: In CRC, CDKN2A overexpression is associated with unfavorable responses to radiotherapy, an immunosuppressive tumor microenvironment, and poor prognosis. Suppression of CDKN2A expression could potentially improve therapeutic outcomes in CRC patients. More experimental and clinical studies are still needed to verify and further distinguish the similarities and differences between colon cancer and rectal cancer.
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Establishment and validation of a prognostic model for pancreatic cancer utilizing genes of tumor-associated neutrophils.
BACKGROUND: Dysregulation in chemotaxis and activation of neutrophils may trigger cancer. Nevertheless, the function of neutrophils and their mechanism during the prognosis of pancreatic cancer (PC) remain unclear. METHODS: From the databases of The Cancer Genome Atlas (TGCA) and GeneCards, the genes of tumor-associated neutrophils (TANs) were screened out leveraging the differential expression analysis. The constructed prognostic model for PC was analyzed through the least absolute shrinkage and selection operator (LASSO) regression and Cox univariate and multivariate regression. The dataset (GSE62452) provided by the Gene Expression Omnibus (GEO) database served as the validation cohort, and its potential mechanistic pathways and biological functions were analyzed leveraging Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO). The immune infiltration analysis, as well as the drug sensitivity prediction, were conducted. The gene expression in the prognostic model was checked through online databases, including Human Protein Atlas (HPA) and Tumor Immune Single-Cell Hub (TISCH). Finally, the genes were experimentally confirmed by immunohistochemistry (IHC) and qPCR in combination with clinical samples from patients with PC. RESULTS: AIM2, PSCA, IL18BP, and LIPE were four genes of TANs closely linked to the prognosis of PC. A model for the prognosis of PC was established utilizing these 4 genes. The AUC (area under the receiver operating characteristic curve (ROC curve)) values of this model for forecasting the survival rates of patients at 1, 2, and 3 years were 0.774, 0.841, and 0.953, respectively. The immune infiltration analysis revealed that resting dendritic cells (resting DCs), naive B cells, CD8T cells, as well as follicular helper T cells, were highly infiltrated among patients suffering from PC. According to the drug sensitivity analysis, the high-risk pancreatic ductal adenocarcinoma (PAAD) group had a significantly higher inhibitory concentration 50 (IC50) for dactolisib, docetaxel, gemcitabine, and ulixertinib compared to the group by patients with low-risk PAAD. The results of IHC and qPCR confirmed those of bioinformatics analysis. CONCLUSION: New TANs-related biomarkers have been found that effectively forecast the prognosis of patients suffering from PAAD.
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Cuproptosis-related lncRNAs predict prognosis and immune response of thyroid carcinoma.
Objective: To estimate the survival and prognosis of patients with thyroid carcinoma (THCA) based on the Long non-coding RNA (lncRNA) traits linked to cuproptosis and to investigate the connection between the immunological spectrum of THCA and medication sensitivity. Methods: RNA-Seq data and clinical information for THCA were obtained from the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. We built a risk prognosis model by identifying and excluding lncRNAs associated with cuproptosis using Cox regression and LASSO methods. Both possible biological and immune infiltration functions were investigated using Principal Component Analysis (PCA), Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and immunoassays. The sensitivity of the immune response to possible THCA medicines was assessed using ratings for tumor immune dysfunction and exclusion (TIDE) and tumor mutational burden (TMB). Results: Seven cuproptosis-related lncRNAs were used to construct our prognostic prediction model: AC108704.1, DIO3OS, AL157388.1, AL138767.3, STARD13-AS, AC008532.1, and PLBD1-AS1. Using data from TCGA's training, testing, and all groups, Kaplan-Meier and ROC curves demonstrated this feature's adequate predictive validity. Different clinical characteristics have varying effects on cuproptosis-related lncRNA risk models. Further analysis of immune cell infiltration and single sample Gene Set Enrichment Analysis (ssGSEA) supported the possibility that cuproptosis-associated lncRNAs and THCA tumor immunity were closely connected. Significantly, individuals with THCA showed a considerable decline in survival owing to the superposition effect of patients in the high-risk category and high TMB. Additionally, the low-risk group had a higher TIDE score compared with the high-risk group, indicating that these patients had suboptimal immune checkpoint blocking responses. To ensure the accuracy and reliability of our results, we further verified them using several GEO databases. Conclusion: The clinical and risk aspects of cuproptosis-related lncRNAs may aid in determining the prognosis of patients with THCA and improving therapeutic choices.
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A consensus spinal cord cell type atlas across mouse, macaque, and human.
The spinal cord contains evolutionarily conserved cell types critical for motor function, sensory processing, and autonomic regulation, many of which are implicated in diverse neurological diseases and injuries. Yet the field lacks a comprehensive molecular characterization of cellular diversity in human, macaque, and mouse spinal cord. Here, we present a unified, cross-species cell type atlas based on the integration of single-nucleus gene expression, chromatin accessibility, and spatial transcriptomic data from segments within cervical, thoracic, lumbar, and sacral regions, including motor neurons (MNs) sampled across the entire rostro-caudal axis of the macaque spinal cord. Leveraging the spatial distributions of our molecularly defined cell types, we generated a cell type-guided anatomical map of spinal cord laminae and nuclei. We identified both conserved and species-specific cellular features, including gene expression patterns across distinct MN subtypes in the primate spinal cord. Cross-species cis-regulatory analysis and deep learning sequence models dissected the enhancer logic underlying viral targeting, uncovering conserved transcription factor grammar encoding cellular identity. Together, these results establish a unifying molecular and anatomical taxonomy of spinal cord cell types across species.
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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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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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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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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.