Aug 2026· Current Issues in Molecular Biology· Vol 48· 0 citations· 33 references
Medicine
TL;DR
An exploratory, integrative analysis using single-cell RNA sequencing of TNBC patients operationally stratified into Good and Bad Prognosis groups suggests a framework in which tumor-intrinsic DNA damage signaling is associated with MHC-I antigen presentation upregulation and CD8 cytotoxic T-cell engagement, supporting further investigation of this axis.
Abstract
Triple-negative breast cancer (TNBC) is characterized by marked immune microenvironment heterogeneity and variable chemotherapy response, yet the epithelial transcriptional programs governing cytotoxic immune activation remain poorly understood. We performed an exploratory, integrative analysis using single-cell RNA sequencing of 31,962 cells from eight TNBC patients operationally stratified into Good and Bad Prognosis groups based on pathological lymphoid infiltration, a discovery grouping subsequently validated against pathological complete response (pCR) in three independent bulk RNA-seq cohorts. This analysis identified four epithelial transcriptional states. The G5 DNA damage subpopulation—predominantly restricted to Good Prognosis tumors (29.2% vs. 0%)—and the G4 Metabolism subpopulation—2.4-fold enriched in Bad Prognosis—were the primary prognostic signatures. Machine learning validation using nested leave-one-cohort-out (LOCO) cross-validation across 614 samples demonstrated that G4 + G5 raw genes with random forest yielded the largest observed mean AUC of 0.653, though these results are exploratory and do not establish a validated clinical classifier. CellChat ligand–receptor interaction analysis revealed that G5 DNA-damage epithelial cells are the dominant immune activators in Good Prognosis TNBC, predominantly engaging CD8 cytotoxic T cells through MHC-I antigen presentation via HLA-A/B/C/E/F → CD8A/CD8B interactions, the highest-probability signaling pathway identified. Spatial transcriptomics independently validated significantly higher DNA damage and CD8 T-cell scores in Good Prognosis tissue. Together, these exploratory findings suggest a framework in which tumor-intrinsic DNA damage signaling is associated with MHC-I antigen presentation upregulation and CD8 cytotoxic T-cell engagement, supporting further investigation of this axis and its potential implications for combining DNA-damaging chemotherapy with immune checkpoint blockade in TNBC.
Immune escape drives cancer progression and therapy resistance, yet its prognostic role and impact on the tumor immune microenvironment in thyroid cancer remain unclear. We integrated single-cell and bulk RNA sequencing data to systematically characterize immune escape and its clinical significance. scRNA-seq analysis characterized cellular heterogeneity and quantified immune escape activity via AUCell. A prognostic gene signature was constructed from differential expression analysis combined with univariate Cox and LASSO regression, and validated using Kaplan-Meier and time-dependent ROC analyses. The immune landscape was profiled using ssGSEA, CIBERSORT, and ESTIMATE, while immunophenoscore (IPS) was used to predict immunotherapy responsiveness. Functional enrichment, CellChat, SCISSOR, tumor mutation burden (TMB), and CellMiner analyses were further performed to explore underlying mechanisms and therapeutic implications. A three-gene signature (CD9, NPC2, PSMB9) effectively stratified patients into highand low-risk groups with distinct survival outcomes. Low-risk tumors exhibited an "immune-hot" phenotype with increased CD8+ T cells and activated NK cells, higher checkpoint expression, and elevated IPS, suggesting greater immunotherapy sensitivity. In contrast, high-risk tumors showed an immune-cold microenvironment with M2 macrophage enrichment. Despite higher TMB, high-risk tumors displayed reduced immune activity, indicating impaired immune recognition. Single-cell analysis further identified MIF and CCL signaling as key mediators of multicellular immune evasion. Overall, our single-cell-informed immune escape signature provides a promising framework for thyroid carcinoma risk stratification and offers insights into personalized immunotherapy.
Ling Jiang, Liu-Yi Zou, Fei-Qi Liu et al.· Korean Journal of Physiology...· 0 citations
Objective
Breast cancer remains a leading cause of global cancer mortality, characterized by profound heterogeneity. While immune checkpoint blockade (ICB) has transformed oncology, its efficacy in breast cancer is often hindered by "immune-cold" microenvironments and immune exclusion. Programmed cell death (PCD) is a critical regulator of tumor immune microenvironment (TIME). However, its role in the breast cancer immune microenvironment remains poorly understood.
Methods
We integrated multi-omics data from six breast cancer cohorts (N=3,764) to develop a programmed cell death learning signature (PCDsig) using over 100 machine learning combinations. The model was benchmarked against 29 published signatures. Single-cell transcriptomic analysis decoded the immune landscape and cellular crosstalk. The role of adaptor-related protein complex 1 subunit sigma 1 (AP1S1) was validated through a clinical cohort, in vitro functional assays, and in vivo syngeneic mouse models.
Results
PCDsig significantly stratified patient prognosis across all cohorts, consistently outperforming 29 existing models. High PCDsig scores correlated with immune-excluded phenotypes, reduced CD8+ T cell infiltration, and lower immunophenoscores. Single-cell analysis revealed that high-PCDsig tumors utilize vascular endothelial growth factor A (VEGFA) signaling to foster an immunosuppressive microenvironment. AP1S1 was identified as the core driver of immune exclusion. And our clinical cohort supported the immune exclusion effect of AP1S1. AP1S1 knockdown impaired tumor progression in vitro and fundamentally remodeled the tumor immune ecosystem in vivo. Combining AP1S1 inhibition with anti-programmed cell death ligand 1 (anti-PD-L1) therapy exerted profound synergistic effects, driven by massive infiltration and functional activation of cytotoxic Granzyme B (GZMB)+CD8+ T cells.
Conclusions
Our study establishes the PCDsig we developed is a potential prognostic and predictive biomarker for breast cancer. We provide the first evidence of AP1S1 as a core immunomodulatory oncogene that mediates immune exclusion. Targeting AP1S1 represents a highly promising strategy to sensitize cold breast tumors to ICB, offering a new perspective for precision immunotherapy.
Gui-Xin Wang, Jun Cao, Parhat Kaysar et al.· Chinese journal of cancer re...· 0 citations
BACKGROUND
High-grade serous ovarian carcinoma (HGSOC) features extensive intratumoral heterogeneity, frequent chemoresistance and poor prognosis. Tumor proliferation kinetics reflected by tumor doubling time (TDT) are linked to therapeutic response, yet molecular drivers of chemoresistance-associated proliferation remain incompletely defined.
METHODS
We integrated scRNA-seq (GSE154600) with bulk data (TCGA-OV, GTEx). Differential analyses of refractory/resistant versus sensitive tumors and tumor versus normal tissues, intersected with TDT-associated genes, identified 105 chemoresistance-associated proliferation genes. Consensus clustering and immune analyses were performed. A 117-algorithm machine-learning framework constructed a prognostic signature trained on TCGA-OV and validated in GSE26193. Survival was assessed by Kaplan-Meier Plotter. Hub genes were validated by qPCR and Western blot in paired sensitive (A2780, TYKnu) and cisplatin-resistant (A2780-DDP, TYKnu-DDP) cell lines.
RESULTS
The genes enriched in cell-cycle and p53 signaling. Consensus clustering defined C1 and C2 subtypes with distinct immune microenvironments; C2 showed upregulation of DNA replication and cell-cycle programs. A nine-gene signature (BIRC5, CENPH, CKAP2, PAK1IP1, PBK, SDF2L1, TEAD4, TPM3, UBE2T) was established. High CENPH, CKAP2, PBK, TEAD4, TPM3 and UBE2T associated with inferior overall survival, while SDF2L1 was protective. These genes were predominantly expressed in malignant epithelial cells, SPP1+/TREM2+ macrophages and CAFs. qPCR confirmed upregulation of six genes in TYKnu-DDP cells; Western blot validated elevated SURVIVIN (BIRC5), CENPH, CKAP2, PAK1IP1 and PBK in resistant lines.
CONCLUSIONS
This single-cell landscape of chemoresistance-associated proliferation genes delineates a nine-gene prognostic signature for HGSOC and confirms hub-gene overexpression in cisplatin-resistant cells, nominating therapeutic targets.
Yan-Min Zhang, Si-Huang Wu, Hui-Zhu Lin et al.· Translational Oncology· 0 citations
Glioblastoma (GBM) is the most common primary intracranial malignancy in adults, characterized by poor survival and high mortality. Emerging evidence suggests that macrophage-associated programmed cell death (MacPCD) plays a critical role in GBM pathogenesis. However, the underlying mechanisms remain poorly understood. This study aimed to identify MacPCD-related prognostic genes in GBM and explore their functional roles.
Transcriptomic data from the GSE68848 dataset were integrated with Macrophage-associated programmed cell death-related genes (MacPCD-RGs) to identify differentially expressed genes (DEGs). Univariate Cox and LASSO regression analyses were performed using the TCGA-GBM training set to construct a prognostic risk model. Beyond prognostic stratification, we conducted a comprehensive multi-omic landscape analysis, including gene set enrichment analysis (GSEA), tumor microenvironment (TME) characterization, tumor mutational burden (TMB) assessment, immunotherapy response prediction, and drug sensitivity prediction. Finally, single-cell RNA sequencing (scRNA-seq) was employed to resolve microenvironmental heterogeneity, identify key cell types and elucidate intercellular communication and developmental trajectories.
Analysis of GSE68848 identified 902 DEGs, of which five intersected with MacPCD-RGs.
FN1
and
TIMP1
were subsequently identified as core prognostic markers. The risk model demonstrated superior predictive performance across the CGGA-325 and GSE83300 validation cohorts. Functional analysis linked the risk score to specific signaling pathways,
PTEN
mutations, infiltration of immune cell subsets (e.g. NKT cells) and sensitivity to Trametinib. Tumor-associated macrophages (TAMs) were identified as the key cell type, exhibiting intense interaction with pericytes and enrichment in fructose/mannose metabolism. Furthermore, pseudotime analysis revealed that
FN1
and
TIMP1
expression peaked during the initial stages of TAM differentiation.
This study identified
FN1
and
TIMP1
as pivotal MacPCD-related prognostic genes in GBM. The risk model based on these markers exhibits moderate predictive performance, offering a reliable tool for clinical prognosis and paving the way for personalized immunotherapy strategies.
Gastric cancer (GC) immunotherapy has limited efficacy. We developed a myeloid immunosuppressive niche score (GC-MIS) to quantify immune evasion and assess its association with the tumor microenvironment and patient prognosis.
Two single-cell and three bulk transcriptomic cohorts were analyzed. A 12-gene MDSC-like signature (MIS) and an 8-gene tumor-intrinsic (TU) module were identified, and GC-MIS was defined as the standardized composite score of the two. Bulk cohort analyses included multivariable regression, Cox modeling, and prognostic evaluation (AUC, C-index, calibration, and decision curves). CellChat characterized epithelial–myeloid interactions, and in vitro GC cell lines exposed to inflammatory and hypoxic stimuli with pathway-specific inhibitors were assessed for PD-L1, STAT3 phosphorylation, and chemokine responses.
MIS and TU modules were stable and moderately correlated at the single-cell level. In bulk cohorts, GC-MIS was positively associated with T-cell exhaustion and myeloid abundance, and inversely associated with antigen presentation (adjusted R² = 0.33–0.41). High GC-MIS independently predicted worse survival (pooled HR = 1.32,
P
< 0.001). A GC-MIS–based model combined with clinical variables showed consistent but moderate prognostic performance across cohorts (5-year AUC ≈ 0.66). Pathway analyses implicated JAK–STAT, HIF-1, and PD-1/PD-L1 signaling, and GC-MIS correlated with CXCL8, IL6, and CCL2 communication axes. In vitro, inflammatory and hypoxic stimuli induced PD-L1 and STAT3 activation, which were significantly attenuated by targeted inhibitors (
P
< 0.05).
GC-MIS is a reproducible transcriptional score associated with features of the tumor immune microenvironment and prognosis in gastric cancer. It reflects tumor–myeloid interaction programs and may serve as a prognostic biomarker. The associated inflammatory and hypoxia-linked signaling pathways warrant further investigation in functional and immunotherapy-treated cohorts.
Xing-Hua Zhang, Can Sun, Xi Zhang· BMC Cancer· 0 citations
Triple-negative breast cancer (TNBC) is an aggressive, heterogeneous form of breast cancer with limited specific therapy options, prevalent metastasis and frequent relapse. Re-analysis of single-cell RNA-sequencing data characterizes the diverse cell subtypes within the tumor and microenvironment of TNBC, supporting a luminal progenitor origin for the cancer and providing clues as to the factors involved in progression of the disease. The relative burdens of these subtypes can be deconvolved from bulk RNA-sequencing data, readily identifying the stem-like, mesenchymal and stromal cell subtypes significantly associated with poor survival and enrichment in metastasis. Importantly, these can be simplified to ten-gene signatures with comparable predictive power, notably in response to different therapeutic strategies, which are linked to relative burdens of different subtypes of stromal fibroblasts. The expression level of these signatures could provide a cheap means for selecting therapy strategies in personalized medicine.
G. Davidson, V. Debien, Tom Sexton· bioRxiv· 0 citations
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