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WGCNA reveals a biomarker for cancer-associated fibroblasts to predict prognosis in cervical cancer.
BACKGROUND: Cancer-associated fibroblasts (CAFs) are crucial components of the cervical cancer tumor microenvironment, playing a significant role in cervical cancer progression, treatment resistance, and immune evasion, but whether the expression of CAF-related genes can predict clinical outcomes in cervical cancer is still unknown. In this study, we sought to analyze genes associated with CAFs through weighted gene co-expression network analysis (WGCNA) and to create a predictive model for CAFs in cervical cancer. METHODS: We acquired transcriptome sequencing data and clinical information on cervical cancer patients from the cancer genome atlas (TCGA) and gene expression omnibus (GEO) databases. WGCNA was conducted to identify genes related to CAFs. We developed a prognostic model based on CAF genes in cervical cancer using the least absolute shrinkage and selection operator (LASSO) Cox regression analysis. Single-cell sequencing data analysis and in vivo experiments for validation of hub genes in CAFs. RESULTS: A prognostic model for cervical cancer was developed based on CAF genes including COL4A1 , LAMC1 , RAMP3 , POSTN , and SERPINF1 . Cervical cancer patients were divided into low- and high-risk groups based on the optimal cutoff value. Patients in the high-risk group had a significantly worse prognosis. Single-cell RNA sequencing data revealed that hub genes in the CAFs risk model were expressed mainly in fibroblasts. The real-time fluorescence quantitative polymerase chain reaction (PCR) results revealed a significant difference in the expression levels of COL4A1 , LAMC1 , POSTN , and SERPINF1 between the cancer group and the normal group ( p < 0.05). Consistently, the results of the immunohistochemical tests exhibited notable variations in COL4A1, LAMC1, RAMP3, POSTN, and SERPINF1 expression between the cancer and normal groups ( p < 0.001). CONCLUSION: The CAF risk model for cervical cancer constructed in this study can be used to predict prognosis, while the CAF hub genes can be utilized as crucial markers for cervical cancer prognosis.
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Decoding chromatin regulator-LAIR1+ M2 like macrophage patterns in atherosclerosis and clinical outcomes of Lung adenocarcinoma patients: evidence from artificial intelligence-driven multi omics and in vitro validation.
Atherosclerosis (AS) is a common complication of lung adenocarcinoma (LUAD), but its underlying mechanisms in LUAD remain unclear. This study aimed to decipher the role of chromatin regulators in AS pathogenesis and their association with clinical outcomes in LUAD patients. AS- and chromatin regulator (CR)-associated prognostic indicators were identified in LUAD and analyzed in the TCGA-LUAD cohort using Cox regression and Disease Ontology analysis. CR-related risk subgroups were defined by NMF in the GSE26939 cohort. GSVA and WGCNA, combined with interpretable machine learning, were used to construct a predictive model and identify key genes, which were validated in GSE68465. The molecular features of key genes and their roles in AS were further evaluated. Besides, mechanisms of key genes in M2-like macrophages were assessed at the single-cell level in LUAD patients using cutting-edge analytical frameworks. A deep learning framework and molecular docking were used to screen natural compounds. Finally, co-culture experiments were conducted for validation. CR signatures can guide the LUAD patients LUAD patients into high- and low-AS risk groups and were associated with clinical outcomes. LAIR1 can be considered as an AS-related factor enriched in M2-like macrophages and involved in LUAD progression. Quercetin was identified as potential preventive agent for AS in LUAD patients. CR-associated signatures play an important role in AS pathogenesis and LUAD progression.
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Metabolic reprogramming-associated genomic instability drives colorectal cancer progression via the UBXN1-NF-κB axis.
Colorectal cancer (CRC) is a common malignancy in clinical practice, and its treatment is greatly challenged by tumor heterogeneity. The most prominent features of CRC heterogeneity are differences in metabolic states and genomic instability, which ultimately lead to unfavorable clinical outcomes. Based on this, the present study aimed to investigate the association between metabolic reprogramming and copy number variation (CNV) in CRC using single-cell datasets. By integrating four publicly available single-cell RNA sequencing datasets, a comprehensive single-cell atlas of CRC was constructed. Subsequently, epithelial cells were specifically analyzed, and consensus non-negative matrix factorization (cNMF) was applied to identify six gene expression programs, covering functional modules such as cell cycle, metabolism, inflammatory stress, and immune interaction. Genomic instability was assessed using the inference of copy number variations (InferCNV) analytical tool, which identified malignant epithelial cells characterized by large-scale CNVs. Meanwhile, metabolic pathway activity at the single-cell level was evaluated using the area under the curve cell (AUCell) method, and predictive performance was further assessed using machine learning algorithms. The results demonstrated that metabolic features could effectively predict the malignant state defined by CNVs, achieving an area under the curve (AUC) of 0.985, with protein metabolism and TP53-related pathways contributing most significantly. Further integrative analysis identified 13 metabolism-related genes associated with clinical prognosis, among which UBXN1 was identified as a central node in the protein-protein interaction network. Functional analysis of UBXN1 revealed that it suppresses the NF-κB signaling pathway, thereby regulating the malignant phenotype of CRC cells. In conclusion, this study systematically elucidates the critical link between metabolic features and genomic instability in CRC, suggesting that UBXN1 may serve as a potential therapeutic target.
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CCT6A Promotes Colon Cancer Cell Proliferation, Migration, and Invasion by Modulating Fatty Acid Metabolism and Activating the TGF-β1/Smad Signaling Pathway.
Colon cancer treatment remains a clinical challenge. Chaperonin containing TCP1 subunit 6 A (CCT6A) acts as an oncogene in multiple tumors. In this study, we investigated its roles in colon cancer cells. We analyzed CCT6A expression using single-cell datasets and the Gene Expression Profiling Interactive Analysis based on The Cancer Genome Atlas database. Immunohistochemistry, quantitative reverse transcription polymerase chain reaction, and western blot analysis were used to assess CCT6A expression levels in colon cancer tissues and cell lines. Additionally, specific roles of CCT6A in colon cancer was analyzed using cell counting kit-8, 5-ethynyl-2'-deoxyuridine staining, flow cytometry, wound healing, transwell, boron dipyrromethene staining, western blot analysis, and nude model mice. We found that CCT6A expression levels were significantly elevated in colon cancer tissues compared to those in normal tissues and predicted a worse prognosis. CCT6A induced proliferation, migration, invasion, epithelial-mesenchymal transition, and fatty acid synthesis and suppressed apoptosis in colon cancer cells. Mechanistically, CCT6A promoted colon cancer progression by increasing the cleavage of latency-associated peptide (LAP)-transforming growth factor-β1 (TGF-β1) to mature form of TGF-β and inducing Smad2/3 phosphorylation in colon cancer cells. Overall, CCT6A promoted colon cancer progression by modulating fatty acid metabolism and activating the TGF-β1/Smad signaling, serving as a potential therapeutic target for colon cancer.
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Identification of immune related molecular subtypes and prognosis model for predicting prognosis, drug resistance in cervical squamous cell carcinoma.
Background: One of the features of tumor immunity is the immunosuppressive tumor microenvironment (TME). In this study, TME gene signatures were used to define the characteristics of Cervical squamous cell carcinoma (CESC) immune subtypes and construct a new prognostic model. Methods: Single sample gene set enrichment analysis (ssGSEA) was used to quantify pathway activity. RNA-seq of 291 CESC were obtained from the Cancer Genome Atlas (TCGA) database as a training set. Microarray-based data of 400 cases of CESC were obtained from the Gene Expression Compilation (GEO) database as an independent validation set. 29 TME related gene signatures were consulted from previous study. Consensus Cluster Plus was employed to identify molecular subtype. Univariate cox regression analysis and random survival forest (RSF) were used to establish the immune-related gene risk model based on the TCGA data set of CESC, and the accuracy of prognosis prediction was verified by GEO data set. ESTIMATE algorithm was used to perform immune and matrix scores on the data set. Results: three molecular subtypes (C1, C2, C3) were screened in TCGA-CESC on account of 29 TME gene signatures. Among, C3 with better survival outcome had higher immune related gene signatures, while C1 with worse prognosis time had enhanced matrix related features. Increased immune infiltration, inhibition of tumor related pathways, widespread genomic mutations and prone immunotherapy were observed in C3. Furthermore, a five immune genes signature was constructed and predicted overall survival for CESC, which successfully validated in GSE44001 dataset. A positive phenomenon was observed between five hub genes expressions and methylation. Similarly, high group enriched in matrix related features, while immune related gene signatures were enriched in low group. Immune cell, immune checkpoints genes expression levels were negatively, while most TME gene signatures were positively correlated with Risk Score. In addition, high group was more sensitive to drug resistance. Conclusion: This work identified three distinct immune subtypes and a five genes signature for predicting prognosis in CESC patients, which provided a promising treatment strategy for CESC.
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Analysis of the potential biological value of pyruvate dehydrogenase E1 subunit β in human cancer.
BACKGROUND: The pyruvate dehydrogenase E1 subunit β (PDHB) gene which regulates energy metabolism is located in mitochondria. However, few studies have elucidated the role and mechanism of PDHB in different cancers. AIM: To comprehensive pan-cancer analysis of PDHB was performed based on bioinformatics approaches to explore its tumor diagnostic and prognostic value and tumor immune relevance in cancer. In vitro experiments were performed to examine the biological regulation of PDHB in liver cancer. METHODS: Pan-cancer data related to PDHB were obtained from the Cancer Genome Atlas (TCGA) database. Analysis of the gene expression profiles of PDHB was based on TCGA and Genotype Tissue Expression Dataset databases. Cox regression analysis and Kaplan-Meier methods were used to assess the correlation between PDHB expression and survival prognosis in cancer patients. The correlation between PDHB and receiver operating characteristic diagnostic curve, clinicopathological staging, somatic mutation, tumor mutation burden (TMB), microsatellite instability (MSI), DNA methylation, and drug susceptibility in pan-cancer was also analyzed. Various algorithms were used to analyze the correlation between PDHB and immune cell infiltration and tumor chemotaxis environment, as well as the co-expression analysis of PDHB and immune checkpoint (ICP) genes. The expression and functional phenotype of PDHB in single tumor cells were studied by single-cell sequencing, and the functional enrichment analysis of PDHB-related genes was performed. The study also validated the level of mRNA or protein expression of PDHB in several cancers. Finally, in vitro experiments verified the regulatory effect of PDHB on the proliferation, migration, and invasion of liver cancer. RESULTS: PDHB was significantly and differently expressed in most cancers. PDHB was significantly associated with prognosis in patients with a wide range of cancers, including kidney renal clear cell carcinoma, kidney renal papillary cell carcinoma, breast invasive carcinoma, and brain lower grade glioma. In some cancers, PDHB expression was clearly associated with gene mutations, clinicopathological stages, and expression of TMB, MSI, and ICP genes. The expression of PDHB was closely related to the infiltration of multiple immune cells in the immune microenvironment and the regulation of tumor chemotaxis environment. In addition, single-cell sequencing results showed that PDHB correlated with different biological phenotypes of multiple cancer single cells. This study further demonstrated that down-regulation of PDHB expression inhibited the proliferation, migration, and invasion functions of hepatoma cells. CONCLUSION: As a member of pan-cancer, PDHB may be a novel cancer marker with potential value in diagnosing cancer, predicting prognosis, and in targeted therapy.
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Comprehensive single-cell analysis deciphered microenvironmental dynamics and immune regulator olfactomedin 4 in pathogenesis of gallbladder cancer.
OBJECTIVE: Elucidating complex ecosystems and molecular features of gallbladder cancer (GBC) and benign gallbladder diseases is pivotal to proactive cancer prevention and optimal therapeutic intervention. DESIGN: We performed single-cell transcriptome analysis on 230 737 cells from 15 GBCs, 4 cholecystitis samples, 3 gallbladder polyps, 5 gallbladder adenomas and 16 adjacent normal tissues. Findings were validated through large-scale histological assays, digital spatial profiler multiplexed immunofluorescence (GeoMx), etc. Further molecular mechanism was demonstrated with in vitro and in vivo studies. RESULTS: The cell atlas unveiled an altered immune landscape across different pathological states of gallbladder diseases. GBC featured a more suppressive immune microenvironment with distinct T-cell proliferation patterns and macrophage attributions in different GBC subtypes. Notably, mutual exclusivity between stromal and immune cells was identified and remarkable stromal ecosystem (SC) heterogeneity during GBC progression was unveiled. Specifically, SC1 demonstrated active interaction between Fibro-iCAF and Endo-Tip cells, correlating with poor prognosis. Moreover, epithelium genetic variations within adenocarcinoma (AC) indicated an evolutionary similarity between adenoma and AC. Importantly, our study identified elevated olfactomedin 4 (OLFM4) in epithelial cells as a central player in GBC progression. OLFM4 was related to T-cell malfunction and tumour-associated macrophage infiltration, leading to a worse prognosis in GBC. Further investigations revealed that OLFM4 upregulated programmed death-ligand 1 (PD-L1) expression through the MAPK-AP1 axis, facilitating tumour cell immune evasion. CONCLUSION: These findings offer a valuable resource for understanding the pathogenesis of gallbladder diseases and indicate OLFM4 as a potential biomarker and therapeutic target for GBC.
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WNT Signaling Driven by R-spondin 1 and LGR6 in High-grade Serous Ovarian Cancer.
BACKGROUND/AIM: R-spondins control WNT signaling and RSPO1 and LGR6, two of its receptors, are uniquely expressed at high levels in high-grade serous ovarian cancer (HGSOC). The aim of this study was to assess the interrelations between the expression of the RSPOs and LGRs in HGSOC and in the ovarian surface (OSE) and fallopian tube surface epithelium (FTSE) from which HGSOC arises. MATERIALS AND METHODS: Analysis of TCGA (HGSOC), CCLE (ovary), and other publicly accessed RNA-Seq data using UC San Diego Computational Cancer Analysis Library (CCAL) to perform differential expression analysis, association studies, and gene set inspection using the single-sample GSEA method. Additionally, we employed multiple publicly available databases including StringDB, Human Protein Atlas, and cBioPortal to aid the investigation. RESULTS: Among normal tissues, expression of RSPO1, LGR5 and LGR6 was highest in the fallopian tube. The relative levels of expression of the RSPOs and LGRs in the OSE and FTSE matched those in HGSOC. RSPO1 and LGR6 were highly co-expressed in all three tissues. Gene set enrichment analysis (GSEA) showed that expression of RSPO1 was strongly linked to the enrichment of three separate WNT-driven GO pathways. Analysis of genes that impacted overall survival identified two other immediately adjacent genes that control WNT signaling, KREMEN1 and ZNRF3 whose expression and copy number were coordinately linked. CONCLUSION: RSPO1 and LGR6 are coordinately expressed in HGSOC and the two normal tissues from which this tumor arises, and their expression is linked to WNT signaling pathways known the control cell fate and proliferation.
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Tertiary lymphoid structure-related genes drive tumor microenvironment heterogeneity and prognostic disparities in left- vs. right-sided colon cancer.
BACKGROUND: Colorectal cancer (CRC) exhibits significant heterogeneity between left-sided (LCC) and right-sided (RCC) subtypes, influenced by tertiary lymphoid structures-related genes (TLSRGs). This study aimed to explore TLSRGs' roles in LCC/RCC differences, including expression, mutations, prognosis, and molecular pathways, while developing a prognostic risk model. METHODS: Data were integrated from The Cancer Genome Atlas Colon Adenocarcinoma (TCGA-COAD) (324 samples: 135 LCC, 189 RCC) and Gene Expression Omnibus (GEO) datasets. TLSRGs were analyzed for differential expression, mutations, and prognostic associations using Wilcoxon tests, Cox regression, and gene set variation analysis (GSVA). Consensus clustering identified molecular subtypes. Single-cell analysis involved Seurat/Harmony for clustering and annotation, single-sample gene set enrichment analysis (ssGSEA) for scoring, and CellChat for cell communication. A machine learning-based risk model was constructed by screening ten algorithms (including SuperPC, RSF, CoxBoost, etc.) using the Mime package. The SuperPC algorithm demonstrated the best performance and was built on TLSRGs and related pathways, validated via Concordance index (C-index), Kaplan-Meier (KM) curves, and receiver operating characteristic (ROC) analysis. RESULTS: Integrated analysis revealed significant heterogeneity between LCC and RCC, characterized by distinct somatic mutation profiles, differential expression of 27 TLSRGs, and subtype stratification via TLSRG-based consensus clustering (C1 enriched in RCC, P<0.001, with higher GSVA activity, P<0.001). Single-cell transcriptomics demonstrated reduced T-cell proportions, elevated epithelial cells, and lower tertiary lymphoid structure (TLS) scores in RCC, alongside weakened cell-cell communication strength (notably epithelial-T/B cell interactions). Prognostic genes exhibited location-specificity: FBLN7 [hazard ratio (HR) =7.09 for disease-specific survival (DSS)] and MS4A1 [HR =7.79 for disease-free interval (DFI)] predicted poor survival in LCC, while ICOS was protective in RCC [HR =0.42 for overall survival (OS)]. Drug sensitivity screening identified 22 agents with differential half-maximal inhibitory concentration (IC50) correlated to TLSRG expression, and immune infiltration analysis revealed reduced CD8+ T cells/follicular helper T cells in RCC. An 8-gene prognostic signature (HSPB1, NCOR2, STIL, etc.) derived via SuperPC effectively stratified high-risk patients in TCGA (HR =2.06, P=0.001) and GSE103479 (HR =1.86, P=0.02), with consistent performance across LCC/RCC subgroups. CONCLUSIONS: TLSRGs underpin LCC/RCC heterogeneity in mutations, expression, immunity, and cell interactions. The developed risk model offers robust prognostic value, highlighting potential therapeutic targets for sidedness-specific CRC management.
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OX40L and IL-2 combination strategy for gastric cancer immunotherapy.
BACKGROUND: Gastric cancer (GC) remains a major global health challenge with persistently low remission rates, largely because most patients are diagnosed at advanced stages. Although chemotherapy, targeted therapy, and immune checkpoint inhibitors have achieved progress, their benefits remain limited due to the highly immunosuppressive tumor microenvironment (TME), characterized by inadequate T-cell activation and impaired costimulatory signaling. Recent genomic and single-cell studies highlight the need for strategies that can more effectively enhance antitumor immunity. Given these challenges, this study aimed to evaluate whether dual stimulation with interleukin-2 (IL-2) and OX40 ligand (OX40L) could synergistically enhance T-cell activation in GC. Specifically, we analyzed OX40/OX40L expression patterns in GC tissues and tumor-infiltrating lymphocytes (TILs), assessed the immunostimulatory effects of IL-2 and OX40L in PBMCs and TILs, and tested a recombinant adenoviral vector co-expressing IL-2 and OX40L to determine its ability to potentiate T-cell-mediated cytotoxicity against primary GC tumor cells. METHODS: Based on preliminary analyses of The Cancer Genome Atlas (TCGA) database and single-cell RNA sequencing (scRNA-seq) data, we identified the costimulatory molecules IL-2 and OX40L as potential therapeutic targets. We collected tumor tissues and paired peripheral blood mononuclear cells (PBMCs) from 70 untreated GC patients, and used bioinformatics tools to analyze T cell subpopulations and OX40/OX40L expression in the TME. The immunostimulatory effects of IL-2 and OX40L, used alone or in combination, were evaluated in vitro on PBMCs and TILs. A recombinant adenoviral vector co-expressing IL-2 and OX40L was also constructed to assess its pro-apoptotic effect in primary GC tumor cell cultures. RESULTS: The combination of IL-2 and OX40L significantly enhanced T cell activation in both PBMCs and TILs, and increased the expression of antitumor effector molecules. The IL-2/OX40L adenoviral vector effectively activated TILs, which subsequently induced prominent apoptotic responses in primary GC tumor cells. CONCLUSIONS: Our study demonstrates that a costimulatory immunotherapy strategy combining IL-2 and OX40L significantly enhances antitumor responses in PBMCs and TILs from GC patients, showing strong therapeutic potential as a complementary approach for the treatment of GC.