A robust 17-gene ASIG-based prognostic signature that effectively stratified BRCA patients into high- and low-risk groups and served as an independent prognostic predictor is established, providing a robust tool for patient risk stratification and offering biological insights into senescence-driven microenvironmental remodeling.
Abstract
Background: Cellular senescence exerts a complex influence on BRCA progression and TME remodeling. However, the specific roles of ASIGs in regulating the TME and determining patient outcomes remain unclear. Methods: Using TCGA (training), METABRIC (validation), and single-cell RNA-seq datasets, we systematically characterized ASIGs in BRCA. Prognostic ASIGs were identified to define molecular subtypes and construct a 17-gene LASSO-Cox risk model, which was integrated with clinical factors to develop a prognostic nomogram. Microenvironmental features and cell–cell communication networks were deconstructed using computational deconvolution and single-cell algorithms (SCISSOR and CellChat). Results: We established a robust 17-gene ASIG-based prognostic signature that effectively stratified BRCA patients into high- and low-risk groups and served as an independent prognostic predictor (HR = 3.94, p < 0.001). The nomogram accurately predicted 1-, 3-, and 5-year overall survival. Notably, the two risk groups exhibited strikingly distinct TME landscapes. The low-risk group was characterized by a coordinated, B cell-centric immune network, whereas the high-risk group displayed T cell exhaustion and immunosuppressive myeloid infiltration. Conclusions: The ASIG-based prognostic risk model is independent of traditional clinicopathological factors, providing a robust tool for patient risk stratification and offering biological insights into senescence-driven microenvironmental remodeling.
Background/Objectives: Alternative splicing (AS) contributes substantially to transcriptomic diversity and has emerged as an important regulator of cancer progression. However, the genome-wide characterization of AS events specific to estrogen receptor (ER)-positive breast cancer remains limited. This study aims to comprehensively profile AS in ER-positive breast cancer and identify a prognostic AS signature associated with patient outcome. Methods: Clinical and splicing data (Percent Spliced In values) were obtained from The Cancer Genome Atlas (TCGA) for 737 ER-positive samples. Prognostic AS events were identified using Cox regression analysis. The Least Absolute Shrinkage Selection Operator (LASSO) model was used to construct an AS-based prognostic signature, and a standardized risk score was calculated for each sample. The signature was then evaluated by Kaplan–Meier (KM) analysis and receiver operating characteristic (ROC) curves, in addition to other methods to validate model performance. Furthermore, transcript-level annotation and RNA expression correlation were performed to evaluate biological relevance. Results: Profiling identified 6276 AS events across 4457 genes, with exon skipping (ES) representing the most prevalent class (34.4%). Model analysis established a novel five-event AS prognostic signature (comprising DNAJC14, BAZ2B, PCDHAC1, PCDHA7, and DAPL1). The signature significantly stratified patients into high-risk and low-risk groups for both disease-free survival (DFS; p < 0.001) and overall survival (OS; p < 0.001). HER2-specific analysis demonstrated more consistent performance in HER2-negative patients for both DFS (p < 0.001) and OS (p = 0.018). The model achieved area under the curve (AUC) of 0.804 for 60-month follow-up supporting long-term prognostic performance. Additionally, the signature demonstrated stable and reliable discrimination with a concordance index (C-index) of approximately 0.73 across multiple validation methods. Conclusions: The study identified AS signature with promising prognostic value in ER-positive breast cancer. This highlights the potential of splicing-based models to refine risk stratification beyond conventional gene expression analysis.
Ahmed B. Basudan, Yazeed Alshuweishi, Hamood Alsudais et al.· Genes· 0 citations
The clinical relevance of mitochondrial RNA modification (MRM) in colorectal cancer (CRC), particularly its value for prognostic stratification, has not been fully defined. We integrated bulk transcriptomic profiles, machine learning-based model screening, single-cell analysis, and experimental validation to identify MRM score-associated prognostic genes and construct a CRC risk model. Seven CRC-related prognostic genes were selected: SPARCL1, MGP, PRELP, PALMD, TNS1, ARHGEF25, and PTGIS. These genes were incorporated into a risk signature with favorable prognostic performance, as supported by nomogram-based assessment. Gene Set Enrichment Analysis indicated that cytokine-related processes may participate in CRC progression. TNS1 showed the strongest positive association with natural killer cells (cor = 0.784, P < 0.05) and the strongest inverse association with type 17 T helper cells (cor = -0.279, P < 0.05). Database-based screening predicted 113 candidate compounds targeting CRC. Single-cell analysis further highlighted smooth muscle cells, epithelial cells, endothelial cells, T cells, and B cells as major cellular populations of interest. SPARCL1, MGP, PRELP, and PALMD increased during both early and late B cell maturation, whereas TNS1 and ARHGEF25 were highly expressed across broader cellular contexts. In conclusion, SPARCL1, MGP, PRELP, PALMD, TNS1, ARHGEF25, and PTGIS may represent MRM score-associated prognostic markers in CRC and require further study.
Qingfang Yue, Hongxia Wen, Zeyu Zhang et al.· Computational biology and ch...· 0 citations
SIRPG is identified as an immune-related prognostic hub and context-dependent tumor-cell regulator associated with apoptosis, immune communication and spatial microenvironmental organization in HNSCC.
Jiaqi Tang, Yu-Lun He, Yuqi Wang et al.· Frontiers in Immunology· 0 citations
Cellular senescence (CS) is increasingly recognized as an important cell-state programme involved in breast cancer progression and therapeutic response, but its context-dependent molecular heterogeneity limits its application in prognostic assessment. In this study, GeneCompass-based all-gene in silico perturbation analysis was performed to identify candidate genes predicted to induce senescence or rejuvenation, thereby expanding the known senescence-related gene set. Machine learning further established a seven-gene prognostic signature that may serve as an adjunctive tool for prognostic assessment across multiple cohorts. The time-dependent AUCs at 1, 3, and 5 years were 0.707, 0.700, and 0.684 in the training cohort; 0.657, 0.661, and 0.629 in the test cohort; and 0.611, 0.646, and 0.637 in the external validation cohort, respectively. Single-cell and spatial transcriptomic analyses suggested that the risk component of the prognostic signature reflects not only malignant epithelial cell states but also stromal–vascular remodelling in the tumor microenvironment. Among the signature genes, ADGRF5 exhibited the most pronounced expression alteration, and its knockdown suppressed malignant phenotypes in breast cancer cells. These findings provide an AI-assisted strategy for senescence biomarker discovery and highlight ADGRF5 as a candidate functional risk gene associated with breast cancer progression.
Clear cell renal cell carcinoma (ccRCC) exhibits pronounced molecular heterogeneity and variable clinical outcomes, complicating prognosis and treatment. While many prognostic models exist, signatures centered on ribosomal RNA (rRNA) processing remain underexplored. This study aimed to establish a prognostic signature associated with rRNA processing and to investigate the functional role of DDX47 in ccRCC. rRNA processing-related genes were curated from Gene Ontology. Prognostic candidates were identified via differential expression and Cox regression in the TCGA-KIRC cohort. A total of 101 machine-learning model combinations were evaluated, and the optimal approach was used to develop the RPPS, with its predictive performance further confirmed in the E-MTAB-1980 and GSE167573 datasets. Prognostic accuracy was evaluated by Kaplan-Meier, ROC, Cox regression, calibration, and decision curve analyses. Immune features, drug sensitivity, and immune escape potential were assessed. Single-cell datasets were integrated to compute an rRNA processing activity score (RPAS), followed by cell–cell communication and pseudotime analyses. DDX47 was knocked out in B cells via scTenifoldKnk, and the resulting perturbed pathways were investigated. DDX47 function was examined in 786-O and A498 cells via siRNA knockdown, Western blotting, EdU, and Transwell assays. The eight-gene RPPS effectively stratified patients into high- and low-risk groups, yielding significantly distinct survival outcomes across all cohorts. The Riskscore independently predicted overall survival and enhanced a nomogram incorporating Age and Stage. High-risk patients showed increased immune infiltration, higher TIDE scores, distinct mutation patterns, and lower predicted IC50 values for selected TKIs. RPAS-low tumor cells displayed enhanced SPP1-mediated communication. Given its high B-cell expression, virtual knockout of DDX47 via scTenifoldKnk revealed enrichment of perturbed genes in immune-related processes, suggesting that DDX47 may be involved in immune-related regulatory programs. High DDX47 expression predicted poor prognosis, and its knockdown suppressed ccRCC cell proliferation, migration, and invasion. The RPPS provides a potential tool for individualized prognostic assessment and molecular stratification in ccRCC. RPAS-defined tumor-cell states may contribute to ccRCC progression through distinct mechanisms, while DDX47 may serve as a candidate prognostic biomarker and warrants further mechanistic investigation in ccRCC.
Jian-Lin Liu, Heng-Xing Tan, Jia-Qing Yang et al.· BMC Cancer· 0 citations
The six-gene ubiquitination-related signature may provide a valuable reference for potential therapeutic targets and prognosis of BC, however, prospective and experimental validation is still required.
Zhi-Biao Li, Ting Yan, Chuxiang Hu et al.· Current Medicinal Chemistry· 0 citations
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