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Molecular landscape of tumor-associated tissue-resident memory T cells in tumor microenvironment of hepatocellular carcinoma.
BACKGROUND: Immunotherapy for liver cancer is used to rejuvenate tumor-infiltrating lymphocytes by modulating the immune microenvironment. Thus, early protective functions of T cell subtypes with tissue-specific residency have been studied in the tumor microenvironment (TME). We identified tumor-associated tissue-resident memory T (TA-TRM) cells in hepatocellular carcinoma (HCC) and characterized their molecular signatures. METHODS: We obtained single-cell RNA and single-cell TCR sequencing data from five patients with HCC. The heterogeneous characteristics of TRM cell subsets within the TME were then investigated and validated. Risk scores were calculated for survival analysis using significant core marker genes based on data from The Cancer Genome Atlas and the International Cancer Genome Consortium. The signaling pathways, trajectories, and clonal diversity of TA-TRM cells were investigated. RESULTS: We characterized two TRM clusters (CD69+ and CD103+) that expressed unique signature genes and validated their similar molecular patterns in an independent dataset. Risk scores based on core gene expression in TA-TRM cells were associated with survival in both datasets. Trajectory analysis revealed that the two lineages followed different trajectory paths with distinct marker gene expression across pseudo-time. CD103+ TA-TRM cells showed diverse clonotypes and shared clonotypes with other cell groups. Lower clonal diversity and distinct signaling interactions were observed in the recurrent than in the non-recurrent samples. The CXCL13-CXCR3 interaction between CD103+ TA-TRM and regulatory T cells was observed only in the recurrent samples. CONCLUSIONS: We identified two subtypes of TA-TRM cells in HCC and demonstrated their unique molecular signatures, relevance to survival, and distinct signaling networks according to recurrence. The study findings provide a better understanding of the molecular characteristics of TA-TRM cells in HCC and potential immunotherapeutic strategies.
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Identification and Validation in a Novel Classification of Helicase Patterns for the Prediction of Tumor Proliferation and Prognosis.
BACKGROUND: Helicases have been classified as a class of enzymes that determine the stability of the cellular genome. There is growing evidence that helicases can help tumor cells resist drug killing by repairing Deoxyribose Nucleic Acid (DNA) or stabilizing transcription, which may contribute to the understanding of drug resistance. Currently, identifying cancer biomarkers among helicases and stratifying patients according to helicase activity will be able to guide treatment well. METHODS: We clustered 371 hepatocellular carcinoma (HCC) patients from The Cancer Genome Atlas (TCGA) by consensus clustering based on helicase expression profiles to identify potential molecular subtypes. The Multiscale Embedded Gene Co-Expression Network Analysis (MEGENA) algorithm was used to find core differential gene modules between different molecular subtypes, and single-cell analysis was utlized to explore the potential function of hub gene. Immunohistochemical (IHC) staining was used to verify the diagnostic value of DDX56 and its ability to reflect the proliferation efficiency of cancer cells. RESULTS: We identified two subtypes associated with helicase. High helicase subtype was associated with poor clinical outcome, massive M0 macrophage infiltration, and highly active cell proliferation features. In addition, we identified a new biomarker, DDX56, which has not been reported in HCC, was highly expressed in HCC tissues, associated with poor prognosis, and was also shown to be associated with high cell proliferative activity. CONCLUSION: In conclusion, based on helicase expression profiles, we have developed a new classification system for HCC, which is a proliferation-related system, and has clinical significance in evaluating prognosis and treating HCC patients, including immunotherapy and chemotherapy. In addition, we identified a new biomarker, DDX 56, which is overexpressed in HCC tissues, predicts a poor prognosis and is a validated index of tumor cell proliferation.
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Single-cell transcriptomics reveal the heterogeneity and dynamic of cancer stem-like cells during breast tumor progression.
Breast cancer stem-like cells (BCSCs) play vital roles in tumorigenesis and progression. However, the origin and dynamic changes of BCSCs are still to be elucidated. Using the breast cancer mouse model MMTV-PyMT, we constructed a single-cell atlas of 31,778 cells from four distinct stages of tumor progression (hyperplasia, adenoma/MIN, early carcinoma and late carcinoma), during which malignant transition occurs. We identified that the precise cell type of ERlow epithelial cell lineage gave rise to the tumors, and the differentiation of ERhigh epithelial cell lineage was blocked. Furthermore, we discovered a specific signature with a continuum of gene expression profiles along the tumor progression and significantly correlated with clinical outcomes, and we also found a stem-like cell cluster existed among ERlow epithelial cells. Further clustering on this stem-like cluster showed several sub-clusters indicating heterogeneity of stem-like epithelial cells. Moreover, we distinguished normal and cancer stem-like cells in this stem-like epithelial cell cluster and profiled the molecular portraits from normal stem-like cell to cancer stem-like cells during the malignant transition. Finally, we found the diverse immune cell infiltration displayed immunosuppressive characteristics along tumor progression. We also found the specific expression pattern of cytokines and their corresponding cytokine receptors in BCSCs and immune cells, suggesting the possible cross-talk between BCSCs and the immune cells. These data provide a useful resource for illuminating BCSC heterogeneity and the immune cell remodeling during breast tumor progression, and shed new light on transcriptomic dynamics during the progression at the single-cell level.
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Resolving inter- and intra-patient heterogeneity in NPM1-mutated AML at single-cell resolution.
NPM1-mutated AML is one of the largest entities in international classification systems of myeloid neoplasms, which are based on integrating morphologic and clinical data with genomic data. Previous research, however, indicates that bulk transcriptomics-based subtyping may improve prognostication and therapy guidance. Here, we characterized the heterogeneity in NPM1-mutated AML by performing single-cell RNA-sequencing and spectral flow cytometry on 16 AML belonging to three distinct subtypes previously identified by bulk transcriptomics. Using single-cell expression profiling we generated a comprehensive atlas of NPM1-mutated AML, collectively reconstituting complete myelopoiesis. The three NPM1-mutated transcriptional subtypes showed consistent differences in the proportions of myeloid cell clusters with distinct patterns in lineage commitment and maturational arrest. In all samples, leukemic cells were detected across different myeloid cell clusters, indicating that NPM1-mutated AML are heavily skewed but not fully arrested in myelopoiesis. Same-sample multi-color spectral flow cytometry recapitulated these skewing patterns, indicating that the three NPM1-mutated subtypes can be consistently identified across platforms. Moreover, our analyses highlighted differences in the abundance of rare hematopoietic stem cells suggesting that skewing occurs early in myelopoiesis. To conclude, by harnessing single-cell RNA-sequencing and spectral flow cytometry, we provide a detailed description of three distinct and reproducible patterns in lineage skewing in NPM1-mutated AML that may have potential relevance for prognosis and treatment of patients with NPM1-mutated AML.
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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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Multiomics analysis of COL12A1 as a promising prognostic biomarker for immune-related treatment of gastric cancer.
COL12A1, a collagen type XII alpha 1 chain, has an instrumental role in the extracellular matrix (ECM), but its effect on gastric cancer (GC) and clinical significance remain unclear. In this study, co-expressed differentially expressed genes (co-DEGs) were identified using Venn diagrams based on datasets from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) for gastric cancer RNA sequencing. Intersections between co-DEGs and TCGA prognosis-related genes were used to pinpoint genes that were differentially associated with prognosis. COL12A1 emerged as a key candidate gene. We analyzed COL12A1 expression in human GC tissues and explored its association with various clinicopathological parameters. Survival analysis, including overall survival (OS) and disease-free survival (DFS), was conducted using TCGA data, and a prognostic nomogram was constructed. The relative protein-encoding gene expression was assessed via immunohistochemistry (IHC) analysis of four downloaded datasets, along with PCR experiments. COL12A1-related DEGs were further identified using the LinkedOmics database, followed by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses. Multiple databases were analysed for the relationship between COL12A1 and methylation. Immune cell infiltration was evaluated through CIBERSORT and TIMER databases, revealing a strong correlation between COL12A1 expression and immune cell presence in the tumor microenvironment. Single-cell expression analysis using TISCH provided further insights into the cellular context of COL12A1 expression. TISCH was used to analyse the single-cell expression of COL12A1. Finally, this work examined the correlation of COL12A1 with drug sensitivity via the RNAactDrug database. COL12A1 overexpression in gastric cancer was detected and foundto be closely related to clinicopathological features. GO/KEGG functional enrichment analysis revealed that COL12A1 was associated with pathways such as extracellular structure organization, extracellular matrix organization and the collagen-containing extracellular matrix. Analysis of several databases revealed that COL12A1 was associated with gene mutation and methylation. Additionally, as revealed by CIBERSORT profiling and TIMER database analysis, COL12A1 expression is related to immune cell infiltration. Correlation analysis via the RNAactDrug website revealed that COL12A1 is associated with drug sensitivity. In conclusion, COL12A1 is significantly overexpressed in gastric cancer and is associated with tumor progression, immune infiltration, and drug sensitivity. These findings suggest that COL12A1 may serve as a potential diagnostic and therapeutic biomarker for gastric cancer.
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Establishment of multiple machine learning prognostic model for gene differences between primary tumors and lymph nodes in luminal breast cancer.
BACKGROUND: This study aimed to explore the correlation between primary tumors (PT) and paired metastatic lymph nodes (LN) and to develop a predictive model to provide evidence for forecasting patient prognoses. METHODS: We obtained single-cell and bulk transcriptome data from the Gene Expression Omnibus database. Furthermore, mRNA transcriptomic data, encompassing 112 normal tissues and 1066 breast cancer samples, along with survival, clinical, and mutation information for breast cancer patients, were acquired from The Cancer Genome Atlas (TCGA). Employing a machine learning integration framework incorporating ten distinct algorithms, we developed and validated a prognostic model. RESULTS: We constructed a prognostic model named Lymph Node Metastasis-Related Scores (LMRS) using 26 differentially expressed genes trained on eight TCGA datasets. Across validation sets, the model demonstrated a high C-index, signifying its stability and effectiveness, outperforming 64 models from other studies. Notably, cytolytic activity and T cell co-stimulation were downregulated in the high LMRS group, alongside a downregulation of immune cells, including B cells, CD8 + T cells, iDCs, and TILs. Similarly, most immune checkpoints exhibited a decreasing trend with high LMRS expression. Finally, we selected the hub biomarkers PGK1 and HSP90 for pathological verification. Results indicated higher expression levels in PT and LN compared to normal and benign tumors, with higher expression levels in LN than in PT. CONCLUSION: This comprehensive analysis sheds light on gene expression differences between PT and LN in breast cancer, culminating in the development of a multiple-gene prognostic model with high clinical accuracy for prognosis prediction.
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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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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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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.