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Multi-dimension metabolic prognostic model for gastric cancer.
BACKGROUND: Metabolic reprogramming is involved in different stages of tumorigenesis. There are six widely recognized tumor-associated metabolic pathways, including cholesterol catabolism process, fatty acid metabolism, glutamine metabolic process, glycolysis, one carbon metabolic process, and pentose phosphate process. This study aimed to classify gastric cancer patients into different metabolic bio-similar clusters. METHOD: We analyzed six tumor-associated metabolic pathways and calculated the metabolic pathway score through RNA-seq data using single sample gene set enrichment analysis. The consensus clustering analysis was performed to classify patients into different bio-similar clusters by multi-dimensional scaling. Kaplan-Meier curves were presented between different metabolic bio-similar groups for OS analysis. RESULTS: A training set of 370 patients from the Cancer Genome Atlas database with primary gastric cancer was chosen. Patients were classified into four metabolic bio-similar clusters, which were identified as metabolic non-specificity, metabolic-active, cholesterol-silence, and metabolic-silence clusters. Survival analysis showed that patients in metabolic-active cluster and metabolic-silence cluster have significantly poor prognosis than other patients (p=0.031). Patients in metabolic-active cluster and metabolic-silence cluster had significantly higher intra-tumor heterogeneity than other patients (p=0.032). Further analysis was performed in metabolic-active cluster and cholesterol-silence cluster. Three cell-cycle-related pathways, including G2M checkpoints, E2F targets, and MYC targets, were significantly upregulated in metabolic-active cluster than in cholesterol-silence cluster. A validation set of 192 gastric cancer patients from the Gene Expression Omnibus data portal verified that metabolic bio-similar cluster can predict prognosis in gastric cancer. CONCLUSION: Our study established a multi-dimension metabolic prognostic model in gastric cancer, which may be feasible for predicting clinical outcome.
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Ferroptosis-related genes identify tumor immune microenvironment characterization for the prediction of prognosis in cervical cancer.
BACKGROUND: Cervical cancer (CC) is a disease that affects female health; therefore, timely prevention and diagnosis of CC are crucial to decrease its mortality. Ferroptosis, an iron-dependent form of non-apoptotic cell death, is involved in tumor progression. However, the role of ferroptosis-related genes (FRGs) in the immune microenvironment of cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC) remains unclear. METHODS: The data sets of CESC patients, including RNA sequencing (RNA-seq) data and clinical information, were obtained from The Cancer Genome Atlas (TCGA). The ESTIMATE algorithm was used to determine the stromal score, immune score, estimate score, and tumor purity in the CESC patients' data. Additionally, FRGs were identified and used to construct a signature marker for the diagnosis and prognosis of CESC. Patients were assigned to a high- or low-risk group based on their median risk score. The tumor microenvironment (TME), immune infiltration, and functional enrichment were compared between the low- and high-risk groups. Functional analyses, including Gene Ontology (GO) analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis, and single-sample Gene Set Enrichment Analysis (ssGSEA), were conducted to explore the underlying mechanisms in the development and prognosis of CESC. RESULTS: The results showed that the estimate score was suitable for predicting the prognosis of CESC patients. Additionally, a prediction model involving four FRGs [phosphatidylethanolamine-binding protein 1 (PEBP1), dual oxidase 1 (DUOX1), iron-sulfur cluster assembly enzyme (ISCU), and cytochrome b (-245) beta subunit (CYBB)] was constructed. The performance of the prognostic model and significant clinical characteristics in predicting CESC prognosis was subsequently validated. Our results showed that the expression of CYBB affected immune cells. Gene functional enrichment analyses showed that these differentially expressed FRGs were mainly enriched in the immunity-related signaling pathways, which indicated that FRGs might affect the development and prognosis of CC by regulating the immune microenvironment. CONCLUSIONS: The expression profiles of FRGs are closely related to the TME and the prognostic survival of CESC patients. The interaction between ferroptosis and immunity in the development of CC provides new insight into the molecular mechanisms of CC.
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Association of High Tumor-Stroma Ratio with Prostate Cancer Progression: Insights from Clinical and Genomic Data.
BACKGROUND: Tumor stroma ratio (TSR) is a prognostic factor in various cancers, but its role in prostate adenocarcinoma (PRAD) remains unclear. This study investigates TSR's prognostic value in PRAD using clinicopathological data, bulk/single-cell RNA sequencing to explore tumor-stroma interactions and identify therapeutic targets. METHODS: Two PRAD cohorts (The Cancer Genome Atlas cohort, TCGA; Lanzhou University Second Hospital, LUSH) were analyzed for TSR associations with clinicopathological features and biochemical recurrence (BCR). TSR was assessed via digital image analysis and expert pathologist review. Publicly available bulk/single-cell RNA sequencing data were analyzed to identify TSR-associated genes and predict drug targets, pathways, and immunotherapy responses. Quantitative real-time PCR validated mRNA expression. In vitro assays assessed cell proliferation, growth, and migration, while in vivo xenograft assays validated BGN's role in promoting tumorigenesis. RESULTS: TSR significantly correlated with clinicopathological features (age, Gleason score, stage, seminal vesicle invasion, BCR) in both TCGA (n = 453) and LUSH (n = 320) cohorts. High TSR independently predicted BCR in multivariable Cox regression. High TSR was associated with copy number variations, differentially expressed miRNAs/transcription factors, and metabolic pathways. Predicted anti-cancer drug targets, like Ki8751, showed potential benefit in high-TSR patients. High TSR may correlate with poor immunotherapy response. Notably, downregulation of BGN in cancer-associated fibroblasts (CAFs) significantly suppressed cell proliferation, migration, and invasion in vitro, and in vivo xenograft assays confirmed that BGN downregulation inhibited tumor growth. CONCLUSION: This study highlights TSR's prognostic significance in prostate cancer and its association with adverse clinical outcomes and complex tumor-stroma interactions, identifying BGN, a stromal cell-related gene, as a potential therapeutic target for CAFs. However, these findings are limited by the retrospective design, necessitating prospective validation.
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Identification and validation of a novel NK cells-related signature to predict prognosis and immune microenvironment in LUAD.
BACKGROUND: The prevalence and fatality rates of lung cancer are experiencing a rapid escalation. Natural Killer (NK) cells have been established to have a crucial role in both tumor initiation and progression. Nevertheless, uncertainties persist regarding their precise implications in the prognosis of LUAD. METHODS: The data were obtained from reputable sources, such as the Cancer Genome Atlas (TCGA), Gene Expression Omnibus (GEO) database, and our internally generated sequencing data. Utilizing the TCGA data as a background, we selected intersecting genes, validated by cluster analysis, to establish a Cox model and validated it using the GEO datasets. Furthermore, we conducted extensive analyses to investigate the significance of potential biomarkers in relation to immune cell infiltration, single-cell data, differential gene expression, and drug sensitivity. RESULTS: 67 immune-related genes associated with NK cells (NK-IRGs) were identified in the TCGA datasets, whose research potential was demonstrated by cluster analysis. A prognostic signature was identified utilizing the univariate and multivariate Cox model, resulting in the identification of five genes, which was validated using GEO datasets. Additionally, the nomogram's calibration curve demonstrated exceptional concordance between the projected and actual survival rates. Subsequent investigations uncovered that this prognostic signature demonstrated its independence as a risk factor. Notably, in the low-risk group, NK cells exhibited elevated levels of immune checkpoint molecules, indicating heightened sensitivity to immune therapy. These findings highlight the potential of utilizing this signature as a valuable tool in the selection of patients who could benefit from targeted immune interventions.
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Characterization of zinc finger protein 536, a neuroendocrine regulator, using pan-cancer analysis.
BACKGROUND: Previous studies suggested that zinc finger protein 536 (ZNF536) was abundant in the central brain and regulated neuronal differentiation. However, the role of ZNF536 in cancer has remained unclear. METHODS: ZNF536 mutation, copy number alteration, DNA methylation, and RNA expression were explored using public portals. Data from The Cancer Genome Atlas (TCGA) were utilized to analyze pathways and tumor microenvironment (TME), with a focus on prognosis in both TCGA and immunotherapy pan-cancer cohorts. Methylated ZNF536 from small cell lung cancer (SCLC) cell lines were utilized to train with probes for conducting enrichment analysis. Single-cell RNA profile demonstrated the sublocalization and co-expression of ZNF536, and validated its targets by qPCR. RESULTS: Genetic alterations in ZNF536 were found to be high-frequency and a single sample could harbor different variations. ZNF536 at chromosome 19q12 exerted a bypass effect on CCNE1, supported by CRISPR data. For lung cancer, ZNF536 mutation was associated with longer survival in primary lung adenocarcinoma (LUAD), but its prognosis was poor in metastatic LUAD and SCLC. Importantly, ZNF536 mutation and amplification had opposite prognoses in Stand Up To Cancer-Mark Foundation (SU2C-MARK) LUAD cohort. ZNF536 mutation altered the patterns of genomic alterations in tumors, and had distinct impacts on the signaling pathways and TME compared to ZNF536 amplification. Additionally, ZNF536 expression was predominantly in endocrine tumors and brain tissues. High-dimensional analysis supported this finding and further revealed regulators of ZNF536. Considering that the methylation of ZNF536 was involved in the synaptic pathway associated with neuroendocrine neoplasms, demonstrating both diagnostic and prognostic value. Moreover, we experimentally verified ZNF536 upregulated neuroendocrine markers. CONCLUSIONS: Our results showed that ZNF536 alterations in cancer, including variations in copy number, mutation, and methylation. We proved the involvement of ZNF536 in neuroendocrine regulation, and identified highly altered ZNF536 as a potential biomarker for immunotherapy.
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Developing a prognostic risk model based on circulating tumor cell genes to predict prognosis and provide potential therapeutic strategies in colorectal cancer.
BACKGROUND: Colorectal cancer (CRC) is a major cause of cancer-related deaths worldwide. Understanding the genetic and molecular alterations in CRC can improve patient outcomes. Circulating tumor cells (CTCs) are crucial in cancer metastasis and progression. Analyzing the differentially expressed genes (DEGs) between CTCs and CRC may provide us with new therapeutic strategies. Therefore, this study aims to analyze these DEGs to construct a prognostic risk model that predicts the outcomes of CRC patients and guides clinical treatment. METHODS: We analyzed The Cancer Genome Atlas (TCGA) database to identify 1,727 DEGs between CRC and normal samples, and GSE82198 data to find 3,564 DEGs between CTCs and primary CRC samples. Using enrichment analysis, least absolute shrinkage and selection operator (LASSO) regression, and stepwise Cox regression, we derived eight model genes to construct a prognostic risk model. Various algorithms were employed in the immune microenvironment analysis. Integrating clinical factors with risk grouping, we developed a nomogram. We assessed chemotherapy sensitivity and epithelial-mesenchymal transition (EMT) scores in high-/low-risk groups and explored model gene expression at the single-cell level. RESULTS: We constructed a prognostic risk model for CRC based on eight DEGs of CTCs. The model effectively predicted treatment outcomes and correlated closely with actual prognosis. Through immune microenvironment analysis, we revealed differences in immune cell infiltration and checkpoint gene expression among different risk groups. Moreover, patients in the high-risk group showed higher sensitivity to chemotherapy drugs compared to those in the low-risk group. CONCLUSIONS: The prognosis model based on CTCs' DEGs can effectively predict patient outcomes, facilitating precision treatment for patients. This model holds significant guiding implications for immunotherapy and chemotherapy in CRC, offering potential strategies for the clinical treatment of CRC.
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Senescence-related immunosuppressive gene MFAP2 orchestrates stromal-immune crosstalk and immunotherapy resistance in gastric cancer.
BACKGROUND: Gastric cancer (GC) is a leading cause of cancer-related mortality worldwide, with poor prognosis and immune checkpoint inhibitor (ICI) resistance mediated by immune escape. Senescence-related immunosuppressive genes (SRIGs) regulate the tumor microenvironment (TME); however, their hub genes and prognostic value in GC remain unclear. This study aimed to identify hub SRIGs and explore their significance. METHODS: Transcriptomic, single-cell RNA sequencing, and spatial transcriptomic data of GC were obtained from The Cancer Genome Atlas and Gene Expression Omnibus databases. Weighted gene co-expression network analysis and Pearson correlation analysis were used to screen for SRIGs. Eight machine learning algorithms identified the hub SRIG, which was validated by RT-qPCR, immunohistochemistry, and clinical samples. Pseudotime trajectory, CellChat, and molecular docking analyses were used to explore the underlying mechanisms and potential drugs. A pan-cancer analysis was used to verify its generalizability. RESULTS: A senescence-related immunosuppressive signature was constructed, stratifying GC into two subtypes with distinct TME and prognosis. Microfibril-associated protein 2 (MFAP2) was identified as the hub SRIG, significantly upregulated in GC, and an independent prognostic risk factor. It was specifically expressed in extracellular matrix cancer-associated fibroblasts (eCAFs), driving their differentiation and extracellular matrix (ECM) remodeling. MFAP2⁺ eCAFs promoted M2 macrophage polarization via CSF1-CSF1R and C3-C3AR1 axes, mediating immune escape and ICI resistance. AZD4547 and MK-2461 were potential targeted drugs. Pan-cancer analysis confirmed the ubiquitous oncogenic role of MFAP2. CONCLUSIONS: MFAP2 is a hub SRIG linking eCAFs, ECM remodeling, and immune escape in GC. It serves as a reliable prognostic and ICI resistance predictor and potential therapeutic target, providing a basis for precision therapy in GC.
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Cancer-Associated Fibroblasts in Gastric Cancer Regulate Macrophage Polarization through RCN3 Pathway.
OBJECTIVE: To explore the role and molecular mechanism of cancer-associated fibroblasts (CAFs) in the tumor microenvironment of gastric cancer (GC). METHODS: The expression of CAFs in GC patients was first assessed for abundance, and survival analysis was performed. Subsequently, The Cancer Genome Atlas (TCGA) data were used for differential analysis, survival analysis, and EPIC analysis, while single-cell data (GSE183904) were downloaded for differential analysis of CAFs. Clinical data pooling, univariate and multivariate Cox analysis, and immunofluorescence were carried out on clinical GC tissue samples to explore RCN3 expression within patient CAFs. Western blot and quantitative polymerase chain reaction (qPCR) were used to detect the expression of RCN3. The relationship between RCN3, PCSK6, and STAT1 was explored by chromatin immunoprecipitation (CHIP) experiments, and the effects of the genes on macrophage polarization were detected by detecting biomarkers of biological M1/M2. RESULTS: CAFs in GC were found to be significantly higher compared to the normal group. Revealing the results of TCGA differential analysis, it was observed that GC exhibited a substantial upregulation in the expression levels of RCN3. The clinical statistics indicate a positive correlation between an elevated level of RCN3 expression and the T-stage classification of tumor size. In addition, RCN3 was found to have a significant impact on the overall survival of patients with gastric cancer, acting as an independent prognostic indicator. Analysis of single-cell data showed high expression of PCSK6 in macrophages, and immunofluorescence staining of samples from GC patients showed increased expression of PCSK6 on the cell membranes of macrophages in GC tissues. The subsequent cellular experiments confirmed RCN3 protein can regulate the expression of PCSK6, and PCSK6 regulates macrophage polarization through STAT1. CONCLUSIONS: CAFs regulate macrophage polarization through the RCN3/PCSK6/STAT1 pathway in GC.
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A single-cell atlas of CD19 chimeric antigen receptor T cells.
Li et al. present a resource of single-cell RNA sequencing (scRNA-seq) data from the infusion products of relapsed or refractory large B cell lymphoma (rrLBCL) patients treated with standard-of-care axicabtagene ciloleucel and identify features that are significantly different between products from responders and non-responders at 3-month followup by PET/CT, an important landmark for long-term outcomes.
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Integrated Analysis of PSMB8 Expression and Its Potential Roles in Hepatocellular Carcinoma.
BACKGROUND: Hepatocellular carcinoma (HCC) represents a highly aggressive malignancy with significant global health implications. The proteasome subunit beta type-8 (PSMB8) gene, known for its association with hepatitis B virus susceptibility, has emerged as a potential regulator of tumor progression. However, its functional role and clinical significance in HCC remain poorly characterized. METHODS: We conducted a comprehensive multi-omics analysis to elucidate the role of PSMB8 in HCC. PSMB8 expression profiles were derived from The Cancer Genome Atlas and validated using the GSE76427 dataset. Prognostic significance was assessed through Kaplan-Meier survival analysis. Then, we systematically evaluated the relationships between PSMB8 expression and clinicopathological features, somatic mutations, immune cell infiltration, immune regulatory genes, and immune checkpoint responses. Single-cell RNA sequencing data from the Tumor Immune Single-cell Hub database were analyzed to determine cell type-specific PSMB8 expression. Tissue-level validation was performed using multiplex immunofluorescence staining on HCC tissue microarrays. RESULTS: PSMB8 demonstrated significant overexpression in HCC tissues and exhibited strong prognostic value. Single-cell analysis revealed predominant PSMB8 expression in T and B cell populations. Notably, PSMB8 expression showed significant positive correlations with immune checkpoint molecules PD-L1/CD274 and CD27. Functional enrichment analysis implicated PSMB8 in multiple oncogenic pathways, particularly proteasome-related processes. CONCLUSION: Our findings position PSMB8 as a promising prognostic biomarker and potential therapeutic target in HCC. The observed associations with immune checkpoint molecules and proteasomal pathways suggest its potential role in modulating tumor immunity and protein homeostasis, warranting further investigation into its mechanistic contributions to HCC progression.