Development of a reliable risk prognostic model for lung adenocarcinoma based on the genes related to endotheliocyte senescence

Cellular senescence is a hallmark for cancers, particularly in lung adenocarcinoma (LUAD). This study developed a risk model using senescence signature genes for LUAD patients. Based on the RNA-seq, clinical information and mutation data of LUAD patients collected from the TCGA and GEO database, we obtained 102 endotheliocyte senescence-related genes. The "ConsensusClusterPlus" R package was employed for unsupervised cluster analysis, and the "limma" was used for the differentially expressed gene (DEG) analysis. A prognosis model was created by univariate and multivariate Cox regression analysis combined with Lasso regression utilizing the "survival" and "glmnet" packages. KM survival and receiver operator characteristic curve analyses were conducted applying the "survival" and "timeROC" packages. "MCPcounter" package was used for immune infiltration analysis. Immunotherapy response analysis was performed based on the IMvigor210 and GSE78220 cohort, and drug sensitivity was predicted by the "pRRophetic" package. Cell invasion and migration were tested by carrying out Transwell and wound healing assays. According to the results, a total of 32 genes related to endotheliocyte senescence were screened to assign patients into C1 and C2 subtypes. The C2 subtype showed a significantly worse prognosis and an overall higher somatic mutation frequency, which was associated with increased activation of cancer pathways, including Myc_targets2 and angiogenesis. Then, based on the DEGs between the two subtypes, we constructed a five-gene RiskScore model with a strong classification effectiveness for short- and long-term OS prediction. High- and low-risk groups of LUAD patients were classified by the RiskScore. High-risk patients, characterized by lower immune infiltration, had poorer outcomes in both training and validation datasets. The RiskScore was associated with the immunotherapy response in LUAD. Finally, we found that potential drugs such as Cisplatin can benefit high-risk LUAD patients. In-vitro experiments demonstrated that silencing of Angiopoietin-like 4 (ANGPTL4), Gap Junction Protein Beta 3 (GJB3), Family with sequence similarity 83-member A (FAM83A), and Anillin (ANLN) reduced the number of invasive cells and the wound healing rate, while silencing of solute carrier family 34 member 2 (SLC34A2) had the opposite effect. This study, collectively speaking, developed a prognosis model with senescence signature genes to facilitate the diagnosis and treatment of LUAD.

E2F expression profiling-based subtypes in head and neck squamous cell carcinoma: clinical relevance, prognostic implications, and personalized therapy

BACKGROUND

Head and neck squamous cell carcinoma (HNSCC) is a heterogeneous malignancy with poor prognosis. Dysregulation of E2F transcription factors (E2Fs), which control cell proliferation and apoptosis, is implicated in HNSCC pathogenesis. This study explores HNSCC molecular heterogeneity via E2Fs expression, identifies distinct subtypes, and develops a prognostic model that integrates gene expression, immune infiltration, and drug sensitivity.

METHODS

We analyzed the TCGA-HNSC dataset (n = 494) and classified samples based on the expression of eight E2Fs using ConsensusClusterPlus. The optimal number of clusters (k = 2) was determined with the getOptK() function, which assesses cluster stability via internal consistency metrics. Differentially expressed genes between subtypes were identified with limma, and functional annotation was performed using Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway analyses. A prognostic model was constructed using LASSO regression on genes significant in univariate Cox analysis and validated in an independent GSE41613 dataset (n = 97). Immune cell infiltration was estimated using CIBERSORT, and drug sensitivity predicted via pRRophetic. Confounding factors such as HPV and smoking status were not included due to incomplete data.

RESULTS

Two distinct E2F-based subtypes emerged. Cluster 1, characterized by lower E2Fs expression, exhibited poorer overall survival (log-rank, p = 0.035) and was enriched in genes related to epidermal development, keratinocyte differentiation, and IL-17 signaling. In contrast, Cluster 2 showed higher E2Fs expression, better survival, and enrichment in genes associated with DNA replication and repair. Notably, high-risk patients demonstrated increased infiltration of M0 and M2 macrophages (p < 0.05), suggesting an immunosuppressive tumor microenvironment that adversely affects prognosis. Our seven-gene prognostic model (AREG, CXCL14, FAM83E, FDCSP, ARHGAP4, EPHX3, and SPINK6) exhibited robust performance with AUCs of 0.692, 0.673, and 0.679 for 1-, 3-, and 5-year survival, a C-index of 0.66, and good calibration. High-risk patients also showed greater sensitivity to targeted agents such as pazopanib and imatinib.

CONCLUSIONS

These findings reveal two distinct E2F-based molecular subtypes of HNSCC that differ in prognosis, functional pathways, immune infiltration, and drug sensitivity. The prognostic model offers valuable risk stratification and identifies potential biomarkers and therapeutic targets, warranting further experimental and clinical validation.