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Comprehensive bioinformatic analysis constructs a CXCL model for predicting survival and immunotherapy effectiveness in ovarian cancer.
Background: Immunotherapy has limited effectiveness in ovarian cancer (OC) patients, highlighting the need for reliable biomarkers to predict the effectiveness of these treatments. The C-X-C motif chemokine ligands (CXCLs) have been shown to be associated with survival outcomes and immunotherapy efficacy in cancer patients. In this study, we aimed to evaluate the predictive value of 16 CXCLs in OC patients. Methods: We analyzed RNA-seq data from The Cancer Genome Atlas, Gene Expression Omnibus, and UCSC Xena database and conducted survival analysis. Consensus cluster analysis was used to group patients into distinct clusters based on their expression patterns. Biological pathway alterations and immune infiltration patterns were examined across these clusters using gene set variation analysis and single-sample gene set enrichment analysis. We also developed a CXCL scoring model using principal component analysis and evaluated its effectiveness in predicting immunotherapy response by assessing tumor microenvironment cell infiltration, tumor mutational burden estimation, PD-L1/CTLA4 expression, and immunophenoscore analysis (IPS). Results: Most CXCL family genes were overexpressed in OC tissues compared to normal ovarian tissues. Patients were grouped into three distinct CXCL clusters based on their CXCL expression pattern. Additionally, using differentially expressed genes among the CXCL clusters, patients could also be grouped into three gene clusters. The CXCL and gene subtypes effectively predicted survival and immune cell infiltration levels for OC patients. Furthermore, patients with high CXCL scores had significantly better survival outcomes, higher levels of immune cell infiltration, higher IPS, and higher expression of PD-L1/CTLA4 than those with low CXCL scores. Conclusion: The CXCL score has the potential to be a promising biomarker to guide immunotherapy in individual OC patients and predict their clinical outcomes and immunotherapy responses.
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ZNF460 inhibits HMGCL and promotes PI3K pathway in colon cancer.
BACKGROUND: Zinc finger protein 460 (ZNF460) is highly expressed in colon cancer, but its specific mechanism of action and downstream targets have not yet been clarified. Therefore, this study aimed to investigate the expression pattern, prognostic value, and biological function of ZNF460 in colorectal cancer (CRC), and to elucidate its underlying molecular mechanisms. METHODS: Cell Counting Kit-8 (CCK-8) assay, 5-ethynyl-2'-deoxyuridine (EdU) assay, and Western blot assays were used to investigate the biological function of ZNF460 in CRC. Transcriptomic and clinical data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) were analyzed to assess ZNF460 expression, prognostic value, and clinical relevance. Functional enrichment, nomogram construction, single-cell sequencing, cell communication, and pseudotime analyses were conducted using R packages including seurat, cluster profiler, cell chat, and monocle 2. Statistical significance was set at P<0.05, with appropriate tests applied based on data type. RESULTS: ZNF460 expression was upregulated in colon cancer. High expression of ZNF460 was associated with poor clinical stage and prognosis of patients. Cell experiments revealed the role of ZNF460 in promoting colon cancer cell proliferation. Single-cell sequencing revealed that ZNF460 had close interactions with malignant, plasma, endothelial, myofibroblasts and other cells. Bioinformatics analysis and in vitro experiments found that ZNF460 inhibited the expression of hydroxymethyl glutaryl-CoA lyase (HMGCL) and increased the phosphorylation level of phosphatidylinositol-3-kinase (PI3K). CONCLUSIONS: ZNF460 is highly expressed in CRC and is associated with poor prognosis. ZNF460 promotes cancer cell proliferation and participates in tumor cell communication. Mechanistically, ZNF460 activates the PI3K pathway by inhibiting HMGCL expression, thereby promoting the progression of CRC, suggesting that it is a potential therapeutic target.
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Determination of Breast Metabolic Phenotypes and Their Associations With Immunotherapy and Drug-Targeted Therapy: Analysis of Single-Cell and Bulk Sequences.
Breast cancer is highly prevalent and fatal worldwide. Currently, breast cancer classification is based on the presence of estrogen, progesterone, and human epidermal growth factor 2. Because cancer and metabolism are closely related, we established a breast cancer classification system based on the metabolic gene expression profile. We performed typing of metabolism-related genes using The Cancer Genome Atlas-Breast Cancer and 2010 (YAU). We included 2,752 metabolic genes reported in previous literature, and the genes were further identified according to statistically significant variance and univariate Cox analyses. These prognostic metabolic genes were used for non-negative matrix factorization (NMF) clustering. Then, we identified characteristic genes in each metabolic subtype using differential analysis. The top 30 characteristic genes in each subtype were selected for signature construction based on statistical parameters. We attempted to identify standard metabolic signatures that could be used for other cohorts for metabolic typing. Subsequently, to demonstrate the effectiveness of the 90 Signature, NTP and NMF dimensional-reduction clustering were used to analyze these results. The reliability of the 90 Signature was verified by comparing the results of the two-dimensionality reduction clusters. Finally, the submap method was used to determine that the C1 metabolic subtype group was sensitive to immunotherapy and more sensitive to the targeted drug sunitinib. This study provides a theoretical basis for diagnosing and treating breast cancer.
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Clinical and genomic landscape of hepatocellular carcinoma subtypes with various proportions of nonleukocyte stromal cells.
BACKGROUND: Hepatocellular carcinoma (HCC) is one of the most common malignancies and inflicts high mortality worldwide. The effect of tumor microenvironment components on HCC oncogenesis remains unknown. In particular, the nonleukocyte portion of the stromal fraction (SF) is poorly understood. METHODS: We comprehensively evaluated the proportional cell counts and gene expression data from The Cancer Genome Atlas (TCGA) to examine the contributions of cell components to the tumor microenvironment. Single-cell sequencing data from the Gene Expression Omnibus (GEO) were also analyzed to verify the association between the nonleukocyte SF and genes. We classified HCC using a hierarchical clustering method based on diversity of nonleukocyte SF-related gene expression among different components, and we used an appropriate GEO dataset to verify the clusters with a support vector machine (SVM) model. The prognosis of subtypes and their relationship with tumor microenvironmental cell proportions, clinicopathogenesis factors, and other indicators were evaluated. RESULTS: Based on linear regression, 711 genes related to nonleukocyte SF were selected from the TCGA dataset. We classified HCC into three subtypes using genes related to the nonleukocyte SF. Additionally, the GEO single-cell sequencing data confirmed the relationship between genes and the nonleukocyte SF. The tumor microenvironment of Type 2 contained the most significant mutually reinforcing interaction between the nonleukocyte SF and tumor cells. Meanwhile, Type 2 patients had the poorest prognosis and the most severe tumor-node-metastasis (TNM) stages, histological grades, etc. The analysis based on the GEO dataset verified the classification results with an SVM model. Type 2 was associated with worse clinicopathological characteristics, including tumor grading and staging, than the other types. In addition, the pathway analysis revealed that signals related to the SF and cell proliferation were significantly enhanced in Type 2 compared to the other group, which consisted of Types 1 and 3. CONCLUSION: The nonleukocyte SF in the tumor microenvironment contributed greatly to HCC oncogenesis. We can use these HCC classification criteria to stratify patients into subtypes for personalized treatment.
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Comprehensive Analysis Reveals the Potential Roles of Transcription Factor Dp-1 in Lung Adenocarcinoma.
BACKGROUND: Transcription factor Dp-1 (TFDP1) was overexpressed and interacted with other genes to impact multiple signaling pathways in various human cancers. However, there is less research about the TFDP1 specific roles in lung adenocarcinoma (LUAD). METHODS: We first explored TFDP1 expression levels and relative diseases from a pan-cancer perspective using the ONCOMINE, TIMER, and Open Targets Platform databases. Then, we used UALCAN, GEPIA 2, TCGA-LUAD data, and Kaplan-Meier plotter to examine TFDP1 clinicopathological features and prognosis in LUAD patients. Genomic alterations and DNA methylation analysis were performed by cBioPortal and MethSurv, respectively. Then, we used a cancer single-cell state atlas (CancerSEA) to find TFDP1 functions at a single-cell resolution. LinkedOmics was used to find TFDP1 coexpressed genes, biological processes, and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways. Then, Gene Set Cancer Analysis (GSCA) was used to examine the drug resistence of TFDP1 in LUAD. RESULTS: We found that TFDP1 was overexpressed in most human cancers and related to various diseases, including LUAD. Moreover, LUAD patients with high TFDP1 expression levels might be significantly associated with individual cancer stages and have a poor prognosis. Multivariate analysis revealed that the American Joint Committee on Cancer (AJCC) pathologic stage, AJCC stage T, and AJCC stage N were the independent prognostic factors. LUAD patients with TFDP1 alterations suggested poor overall survival (OS), and disease-free survival (DFS), while hypermethylation might lead to a good prognosis. TFDP1 and its coexpressed genes were enriched in multiple signaling pathways and biological processes involved in the cell cycle, spliceosome, and DNA replication. Furthermore, TFDP1 was strongly positively related to the half-maximal inhibitory concentration (IC50) values of multiple drugs. CONCLUSIONS: In summary, TFDP1 was a possible biomarker and potential therapeutic target for LUAD patients.
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Single-cell decoding of human islet cell type-specific alterations in type 2 diabetes reveals converging genetic- and state-driven β -cell gene expression defects.
Pancreatic islets maintain glucose homeostasis through coordinated action of their constituent endocrine and affiliate cell types and are central to type 2 diabetes (T2D) genetics and pathophysiology. Our understanding of robust human islet cell type-specific alterations in T2D remains limited. Here, we report comprehensive single cell transcriptome profiling of 245,878 human islet cells from a 48-donor cohort spanning non-diabetic (ND), pre-diabetic (PD), and T2D states, identifying 14 distinct cell types detected in every donor from each glycemic state. Cohort analysis reveals ~25-30% loss of functional beta cell mass in T2D vs. ND or PD donors resulting from (1) reduced total beta cell numbers/proportions and (2) reciprocal loss of 'high function' and gain of senescent β -cell subpopulations. We identify in T2D β -cells 511 differentially expressed genes (DEGs), including new (66.5%) and validated genes (e.g., FXYD2, SLC2A2, SYT1), and significant neuronal transmission and vitamin A metabolism pathway alterations. Importantly, we demonstrate newly identified DEG roles in human β -cell viability and/or insulin secretion and link 47 DEGs to diabetes-relevant phenotypes in knockout mice, implicating them as potential causal islet dysfunction genes. Additionally, we nominate as candidate T2D causal genes and therapeutic targets 27 DEGs for which T2D genetic risk variants (GWAS SNPs) and pathophysiology (T2D vs. ND) exert concordant expression effects. We provide this freely accessible atlas for data exploration, analysis, and hypothesis testing. Together, this study provides new genomic resources for and insights into T2D pathophysiology and human islet dysfunction.
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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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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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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.