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Identification of RNMT as an immunotherapeutic and prognostic biomarker: From pan-cancer analysis to lung squamous cell carcinoma validation.
BACKGROUND: Dysregulation of RNA guanine-7 methyltransferase (RNMT) plays a crucial role in the tumor progression and immune responses. However, the detailed role of RNMT in pan-cancer is still unknown. METHODS: Bulk transcriptomic data of pan-cancer were obtained from the Cancer Genome Atlas (TCGA), Genotype-Tissue Expression (GTEx), and Cancer Cell Line Encyclopedia (CCLE) databases. Single-cell transcriptomic and proteomics data of lung squamous cell carcinoma (LUSC) were analyzed in the Tumor Immune Single-cell Hub 2 (TISCH2) and Clinical Proteomic Tumor Analysis Consortium (CPTAC) databases, respectively. The correlation between RNMT expression and cancer prognosis was analyzed by Cox proportional hazards regression and Kaplan-Meier analyses. The correlation of RNMT expression with common immunoregulators, tumor mutation burden (TMB), microsatellite instability (MSI), mismatch repair (MMR), and DNA methyltransferase (DNMT) was analyzed. Additionally, the correlation between RNMT expression and immune infiltration level was evaluated. A total of 1287 machine learning combinations were used to construct prognostic models for LUSC. qRT-PCR and Western blot were used to validate the bioinformatics findings of RNMT upregulation in LUSC. RESULTS: RNMT was widely expressed across different cancers, with significant correlation to prognosis in cancers such as kidney chromophobe (KICH) (p = 0.0033, HR = 7.12), liver hepatocellular carcinoma (LIHC) (p = 0.01, HR = 1.41), and others. Notably, RNMT participates in the regulation of the tumor microenvironment. RNMT expression positively correlated with immune cell expression (Spearman's rank correlation, p < 0.05). Moreover, RNMT expression was strongly associated with immunoregulators, TMB, MSI, MMR, and DNMT in most cancer types. Notably, RNMT expression displayed excellent prognostic and immunological performance in LUSC. The expression of RNMT was mainly enriched in B cells of LUSC tissues. qRT-PCR and Western blot verified the high expression of RNMT in LUSC. CONCLUSION: RNMT expression widely correlated with prognosis and immune infiltration in various tumors, especially LUSC. The RNMT detection may provide a new idea for future tumor immune studies and treatment strategies.
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Integrated bioinformatics analysis of nucleotide metabolism based molecular subtyping and biomarkers in lung adenocarcinoma.
BACKGROUND: Lung adenocarcinoma (LUAD), a predominant subtype of non-small cell lung cancers, continues to challenge treatment outcomes due to its heterogeneity and complex tumor microenvironment (TME). Dysregulation in nucleotide metabolism has been identified as a significant factor in tumorigenesis, suggesting its potential as a therapeutic target. METHODS: This study analyzed LUAD samples from The Cancer Genome Atlas (TCGA) using Non-negative Matrix Factorization (NMF) clustering, Weighted Correlation Network Analysis (WGCNA), and various machine learning techniques. We investigated the role of nucleotide metabolism in relation to clinical features and immune microenvironment through large-scale data analysis and single-cell sequencing. Using in vivo and in vitro experiments such as RT-qPCR, Western Blot, immunohistochemistry, and subcutaneous tumor formation in mice, we further validated the functions of key nucleotide metabolism genes in cell lines and animals. RESULTS: Nucleotide metabolism genes classified LUAD patients into two distinct subtypes with significant prognostic differences. The 'C1' subtype associated with active nucleotide metabolism pathways showed poorer prognosis and a more aggressive tumor phenotype. Furthermore, a nucleotide metabolism-related score (NMRS) calculated from the expression of 28 key genes effectively differentiated between patient outcomes and predicted associations with oncogenic pathways and immune responses. By integrating various immune infiltration algorithms, we delineated the associations between nucleotide metabolism signature genes and the tumor microenvironment, and characterized their distribution differences at the cellular level by analyzing single-cell sequencing dataset related to immunochemotherapy. Finally, we demonstrated the differential expression of the key nucleotide metabolism gene AUNIP acts as an oncogene to promote LUAD cell proliferation and is associated with tumor immune infiltration. CONCLUSION: The study underscores the pivotal role of nucleotide metabolism in LUAD progression and prognosis, highlighting the NMRS as a valuable biomarker for clinical outcomes and therapeutic responses. Specifically, AUNIP functions as a critical oncogene, offering a promising target for novel treatment strategies in LUAD.
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Construction of a Mitochondria-Related Gene Diagnostic Model Based on Integrated Multiomics Data and Functional Validation of ANK2 as a Key Regulator in Colorectal Cancer.
Colorectal cancer (CRC) is one of the most common malignancies of the digestive tract globally, characterized by high incidence, difficulty in early diagnosis, and poor prognosis. Traditional screening methods have limitations in sensitivity and specificity, thus necessitating the development of novel, efficient molecular diagnostic approaches. Recent studies have highlighted the crucial role of mitochondrial dysfunction in the initiation and progression of various cancers, suggesting that mitochondria-related genes (MRGs) could serve as promising diagnostic targets for CRC. In this study, we integrated transcriptomic data from 1174 samples across The Cancer Genome Atlas (TCGA) and multiple Gene Expression Omnibus (GEO) public datasets (GSE21510, GSE44076, and GSE9348) and combined it with MRG data from the MitoCarta3.0 database for a systematic analysis of differentially expressed genes (DEGs). Using LASSO regression and SVM-RFE, two machine learning algorithms, we identified eight key MRGs (ABCG2, ANK2, MACC1, PMAIP1, SLC22A5, SLC25A34, ACAT1, and PDK4) and constructed an early diagnostic model for CRC. Receiver operating characteristic (ROC) curve analysis confirmed the diagnostic efficacy of the model. Gene interaction networks were constructed using GeneMANIA, demonstrating the potential synergistic roles of these genes in regulating cellular metabolism, drug efflux, and immune modulation. CIBERSORT immune cell infiltration analysis revealed significant correlations between these genes and various immune cell subtypes, including T cells, macrophages, and dendritic cells. Further integration of single-cell RNA sequencing data (GSE245552) identified the specific expression patterns of the diagnostic model genes across different cell types. Additionally, we conducted an in-depth investigation of the ANK2 gene. Immunohistochemistry (HPA database), qRT-PCR, and western blotting confirmed the significantly low expression of ANK2 in CRC tissues and cell lines. Moreover, TUNEL and angiogenesis assays showed that overexpression of ANK2 significantly promoted cell apoptosis and inhibited angiogenesis, suggesting that ANK2 may function as a key tumor suppressor in CRC. In conclusion, this study proposes and validates a CRC diagnostic model based on differentially expressed mitochondrial genes. We systematically explored the molecular mechanisms and immune microenvironment correlations of the model and confirmed the biological effects through single-cell and molecular biology experiments. Notably, we highlight the potential regulatory role of ANK2 in the progression of CRC. This research provides theoretical support and new directions for early screening, diagnostic biomarker identification, and targeted therapy strategies for CRC.
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UCHL1-mediated deubiquitination of PARK7 suppresses ferroptosis and enhances tumor progression in triple-negative breast cancer.
Triple-negative breast cancer (TNBC) has a poor prognosis due to the lack of targeted treatment. Previous studies have shown that the deubiquitinase UCHL1 is significantly upregulated in TNBC tissues and positively correlated with shorter overall survival in patients, suggesting that UCHL1 may drive TNBC progression. The latest research suggests that ferroptosis deficiency can promote tumor metastasis, but it is still unclear whether UCHL1 affects TNBC by regulating ferroptosis. Based on the potential role of UCHL1 in ferroptosis, we propose the hypothesis that UCHL1 enhances the survival and invasion ability of TNBC cells by inhibiting ferroptosis. We downloaded the single-cell RNA sequencing dataset for breast cancer (GSE176078) from the Gene Expression Omnibus database and integrated it with The Cancer Genome Atlas data to conduct bioinformatics analysis, focusing on identifying key genes related to TNBC. We performed gene set enrichment analysis (GSEA) to explore the pathways associated with UCHL1. After that, we validated the expression of UCHL1 in TNBC tissues and cell lines and examined its influence on cell proliferation, migration, and invasion through functional experiments. Finally, we explored the downstream targets of UCHL1, utilizing co-immunoprecipitation, western blot, immunofluorescence colocalization, and establishing TNBC xenograft models in nude mice to elucidate its mechanisms in TNBC progression. Public database analysis revealed high levels of UCHL1 in TNBC. Additional research confirmed its overexpression in TNBC tissues and cells, along with significantly increased TNBC cell proliferation, migration, and invasion associated with high UCHL1 expression. GSEA further identified UCHL1 as being predominantly enriched in pathways related to ferroptosis. UCHL1 interacted with Parkinson's disease-related glycosylation enzyme PARK7 and reduced the degradation of PARK7 protein, which was mediated through the ubiquitin-proteasome pathway. Lastly, both in vitro and in vivo studies revealed that the UCHL1-PARK7 axis fostered tumor progression in TNBC by inhibiting ferroptosis. UCHL1 deubiquitinates and stabilizes PARK7, thereby inhibiting ferroptosis and boosting tumor progression in TNBC, indicating the potential of UCHL1 and PARK7 as therapeutic targets for TNBC patients.
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NR3C1 promotes group 4 medulloblastoma invasion via activating VCAN.
Approximately 30% of group 4 medulloblastomas (G4-MBs) present with metastasis at diagnosis, yet the molecular mechanisms remain unclear. To elucidate differences of genes' expression between primary tumors of metastatic (M+) and non-metastatic (M0) G4-MB, we performed multi-omics profiling, including RNA sequencing, proteomics, single-nucleus RNA sequencing (snRNA-seq), and spatial transcriptomics on tumor samples. Integrative analyses identified VCAN, a chondroitin sulfate proteoglycan, as the most significantly up-regulated gene in primary tumors of M+ G4-MB. High expression of this gene was correlated with poor patient prognosis. Functional assays demonstrated that VCAN promotes proliferation and invasion while inhibiting apoptosis. In addition, NR3C1 was predicted as the key activator of VCAN by Single-Cell Regulatory Network Inference and Clustering (SCENIC). High-definition spatial transcriptomics revealed that NR3C1 and VCAN are highly co-expressed within the same spatial domains. Multiplex immunofluorescence confirmed the co-localization, providing spatial evidence of their regulatory interaction. ChIP-qPCR subsequently confirmed direct binding of NR3C1 to the VCAN promoter. Knockdown of NR3C1 or VCAN suppressed invasion and proliferation and induced apoptosis of the tumor cells, which were partially reversed by VCAN overexpression. Together, these findings revealed that the NR3C1-VCAN axis played a pivotal role in the metastatic progression of G4-MB, highlighting a potential therapeutic target for high-risk patients.
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Identification of cell surface markers for acute myeloid leukemia prognosis based on multi-model analysis.
Given the extremely high inter-patient heterogeneity of acute myeloid leukemia (AML), the identification of biomarkers for prognostic assessment and therapeutic guidance is critical. Cell surface markers (CSMs) have been shown to play an important role in AML leukemogenesis and progression. In the current study, we evaluated the prognostic potential of all human CSMs in 130 AML patients from The Cancer Genome Atlas (TCGA) based on differential gene expression analysis and univariable Cox proportional hazards regression analysis. By using multi-model analysis, including Adaptive LASSO regression, LASSO regression, and Elastic Net, we constructed a 9-CSMs prognostic model for risk stratification of the AML patients. The predictive value of the 9-CSMs risk score was further validated at the transcriptome and proteome levels. Multivariable Cox regression analysis showed that the risk score was an independent prognostic factor for the AML patients. The AML patients with high 9-CSMs risk scores had a shorter overall and event-free survival time than those with low scores. Notably, single-cell RNA-sequencing analysis indicated that patients with high 9-CSMs risk scores exhibited chemotherapy resistance. Furthermore, PI3K inhibitors were identified as potential treatments for these high-risk patients. In conclusion, we constructed a 9-CSMs prognostic model that served as an independent prognostic factor for the survival of AML patients and held the potential for guiding drug therapy.
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Single-cell RNA sequencing reveals cellular and molecular immune profile in a Pembrolizumab-responsive PD-L1-negative lung cancer patient.
High expression of PD-L1 predicts PD-1/PD-L1 inhibitor benefit, meanwhile a few PD-L1-negative patients still benefit from these drugs. In this study, we aimed to explore the underlying cellular and molecular characteristics via single-cell sequencing. Before and after treatment with Pembrolizumab, peripheral blood mononuclear cells (PBMCs) were isolated via Ficoll gradient. Thereafter, single-cell RNA sequencing was performed, and clinical significance was validated with The Cancer Genome Atlas (TCGA) cohort. All 3423 cells of 16 clusters were classified into eight cell types, including NKG7+ T, NKG7+ NK, Naïve T, CDC1C+ dendritic cells, CD8+ T cells, B cells, macrophages and erythrocytes. Cell proportion, the clinical significance of differentially expressed genes and significant pathways of NKG7+ T, NKG7+ NK, Naïve T and CD8+ T cells were analyzed. Ubiquitin-mediated proteolysis/cell cycle/natural killer cell-mediated cytotoxicity were identified as PD-1 blockage-responsive pathways in NKG7+ NK cells. Apoptosis/Th1 and Th2 cell differentiation were proposed as Pembrolizumab-affected pathways in NKT cells. In gene level, ID2, PIK3CD, UQCR10, MATK, MZB1, IL7R and TRGC2 showed a significant correlation with PD-1 expression after TCGA dataset validation, which could possess potential as predictive markers for patients with PD-L1-negative lung squamous cell carcinoma who can benefit from Pembrolizumab.
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A comprehensively prognostic and immunological analysis of actin-related protein 2/3 complex subunit 5 in pan-cancer and identification in hepatocellular carcinoma.
BACKGROUND: Actin-related protein 2/3 complex subunit 5 (ARPC5) is one of the members of actin-related protein 2/3 complex and plays an important role in cell migration and invasion. However, little is known about the expression pattern, prognosis value, and biological function of ARPC5 in pan-cancer. Thus, we focus on ARPC5 as cut point to explore a novel prognostic and immunological biomarker for cancers. METHODS: The public databases, including TCGA, GTEx, and UCEC, were used to analyze ARPC5 expression in pan-cancer. The Human Protein Atlas website was applied to obtain the expression of ARPC5 in different tissues, cell lines, and single-cell types. Univariate Cox regression analysis and Kaplan-Meier analysis were used to explore the prognosis value of ARPC5 in various cancers. Spearman's correlation analysis was performed to investigate the association between ARPC5 expression and tumor microenvironment scores, immune cell infiltration, immune-related genes, TMB, MSI, RNA modification genes, DNA methyltransferases, and tumor stemness. Moreover, qPCR, Western blot, and immunohistochemistry were carried out to examine the differential expression of ARPC5 in HCC tissues and cell lines. CCK8, EdU, flow cytometry, wound-healing assays, and transwell assays were conducted to explore its role in tumor proliferation, apoptosis, migration, and invasion among HCC cells. RESULTS: ARPC5 expression was upregulated in most cancer types and significantly associated with worse prognosis in KIRC, KIRP, LGG, and LIHC. mRNA expression of ARPC5 showed low tissue and cell specificity in normal tissues, cell lines, and single-cell types. ARPC5 expression was positively correlated with the tumor microenvironment scores, immune infiltrating cells, immune checkpoint-related genes in most cancers. ARPC5 in STAD and BRCA was positively associated with TMB, MSI, and neoantigens. We also discovered that ARPC5 was correlated with the expression of m1A-related genes, m5C-related genes, m6A-related genes, and DNA methyltransferases. In experiment analyses, we found that ARPC5 was significantly highly expressed in HCC tissues and HCC cells. Functionally, silencing ARPC5 dramatically decreased proliferation, migration, and invasion ability of HCC cells. CONCLUSIONS: ARPC5 expression affects the prognosis of multiple tumors and is closely correlated to tumor immune infiltration and immunotherapy. Furthermore, ARPC5 may function as an oncogene and promote tumor progression in HCC.
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In silico analysis of the immune microenvironment in bladder cancer.
BACKGROUND: Infiltrating immune and stromal cells are vital components of the bladder cancer (BC) microenvironment, which can significantly affect BC progression and outcome. However, the contribution of each subset of tumour-infiltrating immune cells is unclear. The objective of this study was to perform cell phenotyping and transcriptional profiling of the tumour immune microenvironment and analyse the association of distinct cell subsets and genes with BC prognosis. METHODS: Clinical data of 412 patients with BC and 433 transcription files for normal and cancer tissues were downloaded from The Cancer Genome Atlas. The CIBERSORT algorithm was used to determine the relative abundance of 22 immune cell types in each sample and the ESTIMATE algorithm was used to identify differentially expressed genes within the tumour microenvironment of BC, which were subjected to functional enrichment and protein-protein interaction (PPI) analyses. The association of cell subsets and differentially expressed genes with patient survival and clinical parameters was examined by Cox regression analysis and the Kaplan-Meier method. RESULTS: Resting natural killer cells and activated memory CD4+ and CD8+ T cells were associated with favourable patient outcome, whereas resting memory CD4+ T cells were associated with poor outcome. Differential expression analysis revealed 1334 genes influencing both immune and stromal cell scores; of them, 97 were predictive of overall survival in patients with BC. Among the top 10 statistically significant hub genes in the PPI network, CXCL12, FN1, LCK, and CXCR4 were found to be associated with BC prognosis. CONCLUSION: Tumour-infiltrating immune cells and cancer microenvironment-related genes can affect the outcomes of patients and are likely to be important determinants of both prognosis and response to immunotherapy in BC.
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B-cell-specific signatures reveal novel immunophenotyping and therapeutic targets for hepatocellular carcinoma.
BACKGROUND: Immunotherapy presents both promises and challenges in treating hepatocellular carcinoma (HCC) due to its complex immunological microenvironment. The role of B cells, a key part of the immune system, remains uncertain in HCC. AIM: To identify B-cell-specific signatures and reveal novel immunophenotyping and therapeutic targets for HCC. METHODS: Using the Tumor Immune Single-cell Hub 2 database, we identified B-cell-related genes (BRGs) in HCC. Gene enrichment analysis was performed to explore the possible collaboration between B cells and T cells in HCC. We conducted univariate Cox regression analysis using The Cancer Genome Atlas liver HCC collection dataset to find BRGs linked to HCC prognosis. Subsequently, least absolute shrinkage and selection operator regression was utilized to develop a prognostic model with 11 BRGs. The model was validated using the International Cancer Genome Consortium dataset and GSE76427. RESULTS: The risk score derived from the prognostic model emerged as an independent prognostic factor for HCC. Analysis of the immune microenvironment and cell infiltration revealed the immune status of various risk groups, supporting the cooperation of B and T cells in suppressing HCC. The BRGs model identified new molecular subtypes of HCC, each with distinct immune characteristics. Drug sensitivity analysis identified targeted drugs effective for each HCC subtype, enabling precision therapy and guiding clinical decisions. CONCLUSION: We clarified the role of B cells in HCC and propose that the BRGs model offers promising targets for personalized immunotherapy.