An inflammation-associated five-gene expression signature stratifies survival and immune states in lung adenocarcinoma: an integrative public-cohort analysis
Public LUAD transcriptomes identified two inflammation-associated subtypes and a five-gene score comprising CHRDL1, FDCSP, CXCL13, CYP4B1, and S100P that separated survival groups and marked distinct proliferative and immune expression programs.
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
This signature stratified patients by prognosis in two geographically distinct external cohorts and generated testable metabolic and immune hypotheses.
Q. Ma, Jian-Qing Liang, Jin-tian Li et al.· Genes· 0 citations
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
INTRODUCTION
The important roles of T cells in tumor progression support the development of a T cell-associated prognostic model for Head and Neck Squamous Cell Carcinoma (HNSCC).
METHODS
The single-cell RNA-seq (scRNS-seq) data from GSE181919 were processed via the Seurat package for quality control and cell annotation. hdWGCNA was used to identify T cell-associated co-expression modules. Key genes were screened by univariate and LASSO Cox regression in TCGA to build a RiskScore model, followed by validation in the GSE41613 and GSE117973 datasets. The correlations of the RiskScore with the immune microenvironment, predicted immunotherapy response, and drug sensitivity were analyzed, and preliminary in vitro assays were conducted to explore PIM2 function in HNSCC cells.
RESULTS
Ten cell populations were identified in HNSCC tissues, with T cells representing one of the major cell populations. An eight-gene RiskScore model (TNFRSF4, TNFRSF18, PIM2, CORO1B, CUL9, SOD1, ZC3H12D, and TUBA1B) was established, showing moderate but significant prognostic value. High-risk patients exhibited significantly poorer survival. A nomogram integrating the RiskScore and clinical features was further constructed. Additional analyses supported the prognostic value of the model in HPV-negative patients and showed that the RiskScore was independently associated with overall survival after adjustment for tumor purity. C-index comparisons further revealed higher and relatively consistent prognostic discrimination compared with two previously published HNSCC models. The high-risk group exhibited lower ImmuneScore, StromalScore, ESTIMATEScore, higher TIDE scores, and reduced immune infiltration. The RiskScore was significantly correlated with 11 candidate compounds, and preliminary in vitro assays showed that PIM2 knockdown suppressed malignant phenotypes of HNSCC cells.
DISCUSSION
The scRNS-seq and hdWGCNA analyses identified eight T cell-associated module genes related to HNSCC prognosis. High- and low-risk patients stratified by the RiskScore exhibited distinct immune infiltration and angiogenesis in their Tumor Microenvironment (TME). Drug sensitivity analysis predicted candidate compounds for HNSCC treatment, but further experimental and clinical validation is required.
CONCLUSION
The T cell-associated prognostic model was associated with immune microenvironment features and predicted treatment response and may help predict HNSCC prognosis, providing candidate biomarkers for further investigation.
Qing-miao Shi, Zhen-Zhen Qi, Bingyang Shang et al.· Current Medicinal Chemistry· 0 citations
Immunogenic cell death (ICD) links tumor cell demise with antitumor immunity, but the transcriptional features associated with ICD gene expression patterns and their prognostic significance in colorectal cancer (CRC) remain areas of active investigation. This study integrated single-cell and bulk transcriptomic data from TCGA and GEO to characterize ICD gene set-derived transcriptional features in CRC. ICD-associated gene modules were identified through weighted gene co-expression network analysis (WGCNA) independently in colon and rectal cancers. A seven-gene immune contexture signature (ICS) - CD79A, CXCR6, IRF4, ISG20, PLCG2, TIGIT, TRAF1 - was derived using random survival forest, gradient boosting machine, and Lasso-Cox regression. These genes are immune effector molecules rather than canonical ICD mediators (calreticulin, ATP, HMGB1); the signature should be interpreted as an ICD gene set-derived immune contexture score reflecting the immunological correlates of ICD-associated gene expression, not a direct measure of ICD induction. In the TCGA-CRC training cohort, the signature stratified patients (median cutoff: P = 0.001, HR = 1.906) with 1-, 2-, and 5-year AUCs of 0.68, 0.68, and 0.58, respectively. External validation in GSE39582 (n = 561) showed a non-significant trend (P = 0.072, HR = 1.298). Single-cell expression profiling confirmed that all seven genes were predominantly transcribed by immune cells. The risk-score effect was attenuated after adjustment for immune infiltration estimates. This study provides a hypothesis-generating ICD gene set-derived immune contexture framework, but the signature's modest predictive performance, non-significant primary external validation, and correlative nature indicate that independent validation is required before any clinical application.
Shu-Qiong Su, Guo-Zhen Chen, Shi-Yao Yang et al.· Cancer Treatment and Researc...· 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.
