A neutrophil heterogeneity related gene signature predicts prognosis and reveals regulatory mechanism of tumor immune microenvironment in colorectal cancer
A dual-gene model based on neutrophil heterogeneity demonstrated strong predictive performance and functioned as an independent prognostic indicator, and distinct immune and prognostic differences were identified among molecular subtypes.
Abstract
This study aims to construct a prognostic model based on neutrophil heterogeneity by integrating single-cell and bulk transcriptomic data. Additionally, it seeks to explore the features of the immune microenvironment across different risk subgroups to facilitate precise prognostic assessment and individualized immunotherapy for colorectal cancer.
Colorectal cancer transcriptomic datasets from TCGA and GEO were utilized. Single-cell data underwent quality control, clustering, and cell annotation. Cell communication analysis characterized neutrophil signaling interactions. Candidate genes were identified through differential expression analysis and WGCNA, leading to the establishment and validation of a CCL15/CCDC154 dual-gene prognostic model. Differences in immune infiltration, immune checkpoint expression, and immune escape between high- and low-risk groups were analyzed, followed by molecular subtype clustering.
Significant remodeling of the tumor microenvironment was observed, with neutrophils extensively participating in multicellular signaling networks. The dual-gene model demonstrated strong predictive performance and functioned as an independent prognostic indicator. The high-risk group exhibited a pronounced immunosuppressive phenotype, characterized by significant immune escape and upregulated inhibitory immune checkpoints. Notably, distinct immune and prognostic differences were identified among molecular subtypes.
The dual-gene model based on neutrophil heterogeneity facilitates accurate prognostic stratification. The neutrophil-mediated immune microenvironment is closely linked to clinical outcomes, providing a theoretical basis for personalized treatment strategies in colorectal cancer.
Colorectal cancer (CRC) progression is closely associated with chronic inflammation and immune suppression mediated by M2 macrophages. This study systematically analyzed immune-related and macrophage polarization–related genes in CRC by integrating The Cancer Genome Atlas CRC dataset to identify differentially expressed genes. Prognostic genes were identified using regression and machine learning methods, followed by the construction of a risk model. Comprehensive analyses included gene set enrichment, immune microenvironment assessment, somatic mutation profiling, and drug sensitivity evaluation. Single-cell RNA sequencing identified key cell populations, whereas cell–cell communication and pseudotime trajectory analyses further characterized cellular dynamics. RT-qPCR validation preliminarily confirmed the findings. A three-gene signature comprising
FABP4
,
PPARGC1A
, and
LEP
demonstrated strong prognostic value. Risk stratification based on this signature was associated with distinct immune cell profiles, pathway activities, mutational patterns, and drug sensitivity. Single-cell analysis identified macrophages as central interacting cells, with stable expression of the three genes during macrophage differentiation. Experimental validation further confirmed significant downregulation of these genes in CRC tissues. Overall, the
FABP4
/
PPARGC1A
/
LEP
signature and its associated risk model demonstrate strong potential for predicting outcomes in patients with CRC.
Yuan Yuan, Bing-Xi Zhou, Jin Liu et al.· Scientific Reports· 0 citations
A seven-gene immune-related prognostic signature that, combined with clinicopathological parameters, provides a robust tool for individualized survival prediction and may guide precision management in CRC patients is developed and validated.
Colorectal cancer (CRC) exhibits marked molecular and microenvironmental heterogeneity, and the TNM staging system alone does not fully explain differences in prognosis or treatment response. Neutrophil extracellular traps (NETs) and oxidative stress participate in inflammatory remodeling, immune regulation, and tumor progression in CRC. However, transcriptomic studies integrating NET-related programs with oxidative stress-associated signaling for molecular stratification and prognostic modeling remain limited. Transcriptome and clinical data from TCGA-COADREAD (training set, 449 tumor and 39 normal samples) and GSE39582 (validation set, 566 tumor and 19 non-tumor samples) were analyzed. Candidate NET-related and oxidative stress-associated genes were collected from GeneCards, Gene Ontology, and published NETosis signatures, and were further restricted by differential expression analysis (|log2FC| > 0.585, adjusted P < .05) and univariate Cox analysis (P < .05). Consensus clustering, survival analysis, immune infiltration analysis, mutation analysis, and oncoPredict-based drug sensitivity modeling were performed. A multigene prognostic model was constructed using LASSO-Cox regression and evaluated in both cohorts. Two molecular subtypes were identified. C1 showed a better prognosis, higher tumor mutational burden, higher immune checkpoint and immunogenicity scores, and an immune-active phenotype. C2 showed poorer survival, enrichment of NET formation and inflammatory pathways, lower relative neutrophil infiltration by CIBERSORT, and predicted sensitivity to JAK, Wnt/β-catenin, proteasome, and cell-cycle checkpoint inhibitors. The prognostic model separated high- and low-risk patients in both the training and validation cohorts, with 1-, 3-, and 5-year area under the curves of 0.698, 0.711, and 0.645 in TCGA-COADREAD and 0.542, 0.578, and 0.569 in GSE39582. This study proposes NET-related CRC molecular subtypes with oxidative stress-associated biological context and develops a transcriptomic risk model with external validation. The findings suggest subtype-specific immune and therapeutic features, while prospective clinical and functional validation is required before clinical translation.
Yi Wei, Weijian Chu, Chunhui Rao et al.· Medicine· 0 citations
Background Melanoma is highly invasive with poor advanced-stage prognosis and remarkable heterogeneity of the tumor immune microenvironment. Lysine crotonylation regulates tumor progression and immune processes, yet its role in melanoma remains unclear. Purpose This study aims to explore the value of crotonylation-related genes in melanoma. Patients and Methods Transcriptomic data of 471 melanoma samples from the TCGA database were utilized. Consensus clustering was performed based on 2971 crotonylation-related genes. Differential analysis, WGCNA and LASSO regression were combined to construct a prognostic model, followed by analyses of the immune microenvironment and drug sensitivity. Molecular docking and cellular experiments were adopted to investigate the core gene SEPTIN1. Results Melanoma patients were classified into two subtypes (C1 and C2). Patients in the high-risk group of the established prognostic model exhibited shorter overall survival. SEPTIN1 was correlated with prognosis, immune microenvironment and TMZ response. TMZ could downregulate the expression of SEPTIN1, and overexpression of SEPTIN1 reversed the anti-tumor effect of TMZ. Conclusion Expression signatures of crotonylation-related genes can be applied to molecular subtyping, immune microenvironment dissection and prognostic stratification of melanoma, providing potential clues for individualized diagnosis and treatment of melanoma.
Chen Li, Xin-Yu Cui, Yong Yang et al.· International Journal of Gen...· 0 citations
A robust prognostic model based on four RhoGTPase-related prognostic genes was established, effectively stratifying LUAD patients and provides valuable insights into the heterogeneity of LUAD and has the potential to inform personalized therapeutic strategies.
Tian-Chuan Li, Dan-Hong Wu, A. Yang et al.· Scientific Reports· 0 citations
ABSTRACT Tertiary lymphoid structures (TLSs) modulate immune responses in various solid tumors, but their comprehensive role in lung adenocarcinoma (LUAD) remains unclear. In this study, we analyzed RNA‐seq data from 539 LUAD patients in The Cancer Genome Atlas (TCGA) and microarray data from 223 samples from the Gene Expression Omnibus (GEO, GSE13213, and GSE37745). TLS signatures were evaluated via unsupervised consensus clustering based on 12 chemokine transcriptome signatures. The relationships between TLS and clinical characteristics, tumor microenvironment (TME) cell infiltration, and prognosis were assessed using ESTIMATE and CIBERSORT. A prognostic model was established using LASSO regression and validated with external datasets. Additionally, H&E and IHC analyses were performed to explore associations between intratumoral TLS density, immune‐related molecular expression, and patient prognosis in LUAD. Consensus clustering of the TCGA cohort revealed two distinct LUAD patient clusters according to TLS abundance. Cluster 1 exhibited greater immune cell infiltration, more favorable prognosis, and increased expression of immune checkpoint molecules. We developed a prognostic model comprising eight survival‐associated genes that act as independent prognostic factors for patient survival. H&E/IHC analyses revealed that TLS density—regardless of pathological stage—was associated with better prognosis; higher intratumoral TLS density/proportion was also related to more favorable outcomes. IHC confirmed that survival‐associated genes (CD5, HLA‐DMB, and P2RY13) are independent prognostic indicators in LUAD. Our study demonstrated the close relationship between TLS signatures and an active immune microenvironment, highlighting their potential as independent prognostic indicators in LUAD.
Si-Hong Le, Weidan Fang, Y. Le et al.· Cancer Medicine· 0 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
