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Identification of a Prognostic Signature Based on Tumor-Infiltrating B Lymphocyte mRNA in Head and Neck Squamous Cell Carcinoma.
Introduction: Tumor-infiltrating B cells (TILBs) are an important part of the immune response during tumor regulation. However, the significance of B cells in immunotherapy has not been fully determined. Methods: In this study, highly expressed genes in B cells were obtained by comparing the gene expression in B cells with that in other immune cells and were named TILB-mRNAs. Among them, those genes expressed in patients with head and neck squamous cell carcinoma (HNSCC) identified in The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) atlas were employed to screen for genes associated with HNSCC prognosis using univariate Cox analysis, least absolute shrinkage and selection operator (LASSO) regression analysis, and a TILB-related signature was constructed to predict patient prognostic risk using multivariate Cox regression analyses. Results: The constructed TILB-related signature, which comprised seven mRNAs (ZNF439, KMO, KDM5D, IFT57, HDAC9, GSAP, and CCR7), was verified to have a good ability to predict the prognosis of patients with HNSCC using three independent validation datasets from GEO, and the predictive ability was not affected by other factors. The signature reflected the state of immune cell infiltration in tumor tissue, especially B cells, patients with higher risk scores (RSs) had fewer infiltrating immune cells in their tumors, especially B cells. The gene expression of the TILB-related signature was also verified in TILBs from HNSCC using single-cell analysis, revealing that TILB-related marker genes were differentially expressed in different GB cell subsets. Discussion: This study provides risk assessment and outcome prediction for patients with HNSCC and provides potential targets for immunotherapy of HNSCC.
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Multicellular immune dynamics implicate PIM1 as a potential therapeutic target for uveitis.
Uveitis is a severe autoimmune disease, and a common cause of blindness; however, its individual cellular dynamics and pathogenic mechanism remain poorly understood. Herein, by performing single-cell RNA sequencing (scRNA-seq) on experimental autoimmune uveitis (EAU), we identify disease-associated alterations in cell composition and transcriptional regulation as the disease progressed, as well as a disease-related molecule, PIM1. Inhibiting PIM1 reduces the Th17 cell proportion and increases the Treg cell proportion, likely due to regulation of PIM1 to the protein kinase B (AKT)/Forkhead box O1 (FOXO1) pathway. Moreover, inhibiting PIM1 reduces Th17 cell pathogenicity and reduces plasma cell differentiation. Importantly, the upregulation of PIM1 in CD4+ T cells and plasma cells is conserved in a human uveitis, Vogt-Koyanagi-Harada disease (VKH), and inhibition of PIM1 reduces CD4+ T and B cell expansion. Collectively, a dynamic immune cellular atlas during uveitis is developed and implicate that PIM1 may be a potential therapeutic target for VKH.
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Research on the influence of radiotherapy-related genes on immune infiltration, immunotherapy response and prognosis in melanoma based on multi-omics.
BACKGROUND: Skin cutaneous melanoma (SKCM) is a significant oncological challenge due to its aggressive nature and poor treatment outcomes. This study explores the comprehensive effects of radiotherapy (RT) in SKCM, focusing on cell signaling pathways, immune infiltration, immune gene correlations, immunotherapy response, and prognosis. METHODS: Using the Cancer Genome Atlas (TCGA) database, differentially expressed genes (DEGs) in SKCM patients undergoing RT were identified. A risk score model based on these DEGs was developed to assess the effects of RT-related genes on drug sensitivity, immune cell infiltration, immunotherapy response, and prognosis through multi-omics analysis. Human melanoma cells UACC62 and UACC257 were irradiated with 8 Gy gamma ray to establish an in vitro model, verifying the impact of radiotherapy on gene expression. RESULTS: The risk score demonstrated significant prognostic value and emerged as an independent prognostic factor. miRNA-mRNA and transcription factor regulatory networks underscored its clinical significance. Four key genes were identified: DUSP1, CXCL13, SLAMF7, and EVI2B. Analysis of single-cell and immunotherapy datasets indicated that these genes enhance immune response and immunotherapy efficacy in melanoma patients. PCR results confirmed that gamma rays increased the expression of these genes in human melanoma cells UACC62 and UACC257. CONCLUSION: Using a multi-omics approach, we analyzed and validated the impact of RT on the immune landscape of melanoma patients. Our findings highlight the critical role of RT-related genes in predicting SKCM prognosis and guiding personalized therapy strategies, particularly in the context of immunotherapy. These contribute to understanding the role of radiotherapy combined with immunotherapy in melanoma.
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An atlas of genetic effects on cellular composition of the tumor microenvironment.
Deciphering the composition of the tumor microenvironment (TME) is critical for understanding tumorigenesis and to design immunotherapies. In the present study, we mapped genetic effects on cell-type proportions using single-cell and bulk RNA sequencing data, identifying 3,494 immunity quantitative trait loci (immunQTLs) across 23 cancer types from The Cancer Genome Atlas. Functional annotation revealed regulatory potential and we further assigned 1,668 genes that regulate TME composition. We constructed a combined immunQTL map by integrating data from European and Chinese colorectal cancer (CRC) samples. A polygenic risk score that incorporates these immunQTLs and hits on a genome-wide association study outperformed in CRC risk stratification within 447,495 multiethnic individuals. Using large-scale population cohorts, we identified that the immunQTL rs1360948 is associated with CRC risk and prognosis. Mechanistically, the rs1360948-G-allele increases CCL2 expression, recruiting regulatory T cells that can exert immunosuppressive effects on CRC progression. Blocking the CCL2-CCR2 axis enhanced anti-programmed cell death protein 1 ligand therapy. Finally, we have established a database (CancerlmmunityQTL2) to serve the research community and advance our understanding of immunogenomic interactions in cancer pathogenesis.
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Novel prognostic alternative splicing events in colorectal Cancer: Impact on immune infiltration and therapy response.
OBJECTIVE: This study aims to comprehensively analyze alternative splicing (AS) features in colorectal cancer (CRC) using integrative multi-omics and to elucidate their relationship with the CRC immune microenvironment. METHODS: Transcriptomic data, clinical information, and Percent Spliced In (PSI) values of AS events for CRC patients were obtained from The Cancer Genome Atlas (TCGA) and TCGA SpliceSeq databases. Differentially expressed AS events were identified. Univariate Cox analysis was used to pinpoint prognosis-related AS events. A prognostic risk model was developed and validated using multivariate Cox analysis, patient survival analysis, and the area under the receiver operating characteristic (ROC) curve (AUC). Gene Set Enrichment Analysis (GSEA), immune infiltration, immunotherapy, chemotherapy sensitivity analyses, and regulatory relationships between AS events and splicing factors (SFs) were conducted. Single-cell sequencing was used to study the distribution of key factors. siRNA and overexpression vectors were utilized to silence/overexpress BCAS1 in CRC cells and evaluate their effects on cell growth, migration, and invasion. Furthermore, the interaction between BCAS1 and ANO7 pre-mRNA was investigated using RIP-PCR. RESULTS: 82 prognosis-related AS events were identified in CRC patients. A 15-AS prognostic model was constructed, which correlated with immune cell infiltration and showed differences in immunotherapy and chemotherapy sensitivity. BCAS1 was identified as a potential regulator of the ANO7|58341|AT splicing event in CRC. Single-cell sequencing analysis revealed the distribution of BCAS1 and ANO7 in cancer stem cells. In vitro experiments demonstrated that overexpression of BCAS1 and silencing of ANO7 inhibit the proliferation, migration, and invasion of CRC cells. Moreover, BCAS1 suppresses the progression of CRC by modulating ANO7 alternative splicing. CONCLUSION: This study provides new insights into the role of alternative splicing in colorectal cancer, particularly the potential regulatory action of BCAS1 on the ANO7|58341|AT splicing event. It also identifies the impact of alternative splicing on the tumor microenvironment and potential implications for immunotherapy, highlighting its relevance for the in-depth study and treatment of CRC.
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Identification of telomere-related genes in the progression of colon adenocarcinoma: a bioinformatics analysis.
BACKGROUND: Telomeres play a crucial role in chromosomal stability and cancer development. However, the prognostic significance of telomere-related genes (TRGs) in colon adenocarcinoma (COAD) remains unexplored. In this study, we aimed to establish a TRG-based prognostic model for COAD, explore its association with the tumor immune microenvironment and drug sensitivity, and offer new therapeutic targets. METHODS: RNA sequencing (RNA‑Seq) data of COAD with corresponding patient survival data from The Cancer Genome Atlas (TCGA), and TRGs from TelNet were used. A prognostic model was created by univariate/multivariate Cox regression analyses. Meanwhile, a nomogram was created for overall survival (OS) prediction. In addition, immune microenvironment [Cell‑type Identification By Estimating Relative Subsets Of RNA Transcripts (CIBERSORT), Tumor Immune Dysfunction and Exclusion (TIDE), single‑sample Gene Set Enrichment Analysis (ssGSEA), and immune checkpoint gene analysis] and drug sensitivity [half‑maximal inhibitory concentration (IC50)] analyses were performed. RESULTS: This identified six key TRGs (USP2, TRIM7, EPHA6, IP6K3, CALML6, and COCH) significantly associated with patient outcome. A nomogram incorporating these genes demonstrated robust predictive ability for OS, with areas under the curve (AUCs) of 0.746, 0.750, and 0.726 for 1-, 2-, and 3-year OS, respectively. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) functional enrichment analyses revealed that these genes were involved in cancer‑related pathways. Furthermore, distinct immune infiltration patterns, characterized by significant negative correlations between risk scores and activated CD8+ T cells as well as Memory B cells, were observed between high- and low-risk patient groups. Dasatinib, docetaxel, erlotinib, and gefitinib were identified as potential therapeutic candidates for high-risk patients. Finally, the differential expression of EPHA6 between COAD and normal tissues was validated by immunohistochemical (IHC) staining and Western blotting. CONCLUSIONS: Our findings establish TRGs as critical genetic determinants and powerful predictors of COAD prognosis, offering insights into potential therapeutic targets.
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Single-cell transcriptome analyses reveal novel targets modulating cardiac neovascularization by resident endothelial cells following myocardial infarction.
AIMS: A better understanding of the pathways that regulate regeneration of the coronary vasculature is of fundamental importance for the advancement of strategies to treat patients with heart disease. Here, we aimed to investigate the origin and clonal dynamics of endothelial cells (ECs) associated with neovascularization in the adult mouse heart following myocardial infarction (MI). Furthermore, we sought to define murine cardiac endothelial heterogeneity and to characterize the transcriptional profiles of pro-angiogenic resident ECs in the adult mouse heart, at single-cell resolution. METHODS AND RESULTS: An EC-specific multispectral lineage-tracing mouse (Pdgfb-iCreERT2-R26R-Brainbow2.1) was used to demonstrate that structural integrity of adult cardiac endothelium following MI was maintained through clonal proliferation by resident ECs in the infarct border region, without significant contributions from bone marrow cells or endothelial-to-mesenchymal transition. Ten transcriptionally discrete heterogeneous EC states, as well as the pathways through which each endothelial state is likely to enhance neovasculogenesis and tissue regeneration following ischaemic injury were defined. Plasmalemma vesicle-associated protein (Plvap) was selected for further study, which showed an endothelial-specific and increased expression in both the ischaemic mouse and human heart, and played a direct role in regulating human endothelial proliferation in vitro. CONCLUSION: We present a single-cell gene expression atlas of cardiac specific resident ECs, and the transcriptional hierarchy underpinning endogenous vascular repair following MI. These data provide a rich resource that could assist in the development of new therapeutic interventions to augment endogenous myocardial perfusion and enhance regeneration in the injured heart.
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Identification of prognostic biomarkers in colorectal cancer through multi-omics profiling of programmed cell death pathways.
BACKGROUND: Programmed cell death (PCD) pathways, including autophagy, ferroptosis, cuproptosis, neutrophil extracellular trap formation (NETosis), and paraptosis, are central to tumor biology and hold potential as therapeutic targets in colorectal cancer (CRC). The aim of this study is to use multi-omics data to analyze the role of PCD in CRC. METHODS: We conducted a multi-omics analysis using data from The Cancer Genome Atlas (TCGA), single-cell sequencing, and spatial transcriptomics to investigate the expression profiles, prognostic significance, and functional effects of PCD-associated genes in CRC. Key prognostic genes were identified through gene set variation analysis (GSVA), single-sample gene set enrichment analysis (ssGSEA), univariate Cox regression, and least absolute shrinkage and selection operator (LASSO) regression modeling. RESULTS: Our analysis identified five PCD pathways with significant prognostic relevance in CRC, particularly autophagy and cuproptosis. Pan-cancer analyses highlighted unique and shared expression patterns of PCD-related genes across diverse cancer types, revealing their differential roles in cancer progression. Dynamic network biomarker (DNB) modeling pinpointed stage-specific critical transitions in PCD pathway activity, suggesting temporal variations in pathway influence on tumor progression. Functional assays demonstrated that overexpression of nuclear receptor coactivator 4 (NCOA4), an autophagy-related gene, suppressed CRC cell migration and invasion while promoting apoptosis, validating its anti-tumor role in CRC. CONCLUSIONS: This study underscores the prognostic significance of PCD pathways in CRC, highlighting their potential as biomarkers and therapeutic targets. By identifying core genes within these pathways and elucidating their temporal effects on tumor progression, we provide a comprehensive foundation for future research into PCD-targeted therapies in CRC, aiming to enhance personalized treatment strategies.
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Comprehensive Analysis of a Nine-Gene Signature Related to Tumor Microenvironment in Lung Adenocarcinoma.
Lung adenocarcinoma (LUAD) is the most common malignancy, leading to more than 1 million related deaths each year. Due to low long-term survival rates, the exploration of molecular mechanisms underlying LUAD progression and novel prognostic predictors is urgently needed to improve LUAD treatment. In our study, cancer-specific differentially expressed genes (DEGs) were identified using the robust rank aggregation (RRA) method between tumor and normal tissues from six Gene Expression Omnibus databases (GSE43458, GSE62949, GSE68465, GSE115002, GSE116959, and GSE118370), followed by a selection of prognostic modules using weighted gene co-expression network analysis. Univariate Cox regression, least absolute shrinkage and selection operator (LASSO), and multivariate Cox regression analyses were applied to identify nine hub genes (CBFA2T3, CR2, SEL1L3, TM6SF1, TSPAN32, ITGA6, MAPK11, RASA3, and TLR6) that constructed a prognostic risk model. The RNA expressions of nine hub genes were validated in tumor and normal tissues by RNA-sequencing and single-cell RNA-sequencing, while immunohistochemistry staining from the Human Protein Atlas database showed consistent results in the protein levels. The risk model revealed that high-risk patients were associated with poor prognoses, including advanced stages and low survival rates. Furthermore, a multivariate Cox regression analysis suggested that the prognostic risk model could be an independent prognostic factor for LUAD patients. A nomogram that incorporated the signature and clinical features was additionally built for prognostic prediction. Moreover, the levels of hub genes were related to immune cell infiltration in LUAD microenvironments. A CMap analysis identified 13 small molecule drugs as potential agents based on the risk model for LUAD treatment. Thus, we identified a prognostic risk model including CBFA2T3, CR2, SEL1L3, TM6SF1, TSPAN32, ITGA6, MAPK11, RASA3, and TLR6 as novel biomarkers and validated their prognostic and predicted values for LUAD.
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A multi-scale integrated analysis identifies KRT8 as a pan-cancer early biomarker.
An early biomarker would transform our ability to screen and treat patients with cancer. The large amount of multi-scale molecular data in public repositories from various cancers provide unprecedented opportunities to find such a biomarker. However, despite identification of numerous molecular biomarkers using these public data, fewer than 1% have proven robust enough to translate into clinical practice. One of the most important factors affecting the successful translation to clinical practice is lack of real-world patient population heterogeneity in the discovery process. Almost all biomarker studies analyze only a single cohort of patients with the same cancer using a single modality. Recent studies in other diseases have demonstrated the advantage of leveraging biological and technical heterogeneity across multiple independent cohorts to identify robust disease biomarkers. Here we analyzed 17149 samples from patients with one of 23 cancers that were profiled using either DNA methylation, bulk and single-cell gene expression, or protein expression in tumor and serum. First, we analyzed DNA methylation profiles of 9855 samples across 23 cancers from The Cancer Genome Atlas (TCGA). We then examined the gene expression profile of the most significantly hypomethylated gene, KRT8, in 6781 samples from 57 independent microarray datasets from NCBI GEO. KRT8 was significantly over-expressed across cancers except colon cancer (summary effect size=1.05; p < 0.0001). Further, single-cell RNAseq analysis of 7447 single cells from lung tumors showed that genes that significantly correlated with KRT8 (p < 0.05) were involved in p53-related pathways. Immunohistochemistry in tumor biopsies from 294 patients with lung cancer showed that high protein expression of KRT8 is a prognostic marker of poor survival (HR = 1.73, p = 0.01). Finally, detectable KRT8 in serum as measured by ELISA distinguished patients with pancreatic cancer from healthy controls with an AUROC=0.94. In summary, our analysis demonstrates that KRT8 is (1) differentially expressed in several cancers across all molecular modalities and (2) may be useful as a biomarker to identify patients that should be further tested for cancer.