Background. Currently, predictive models were not developed based on the signaling pathway signatures of immune-related lncRNAs in breast cancer (BRCA) patients. Methods. We selected unsupervised hierarchical clustering algorithm to classify patients with BRCA based on the significant immune-derived lncRNAs from the TCGA dataset. And different methods including ESTIMATE, ImmuneCellAI, and CIBERSORT were performed to evaluate the immune infiltration of tumor microenvironment. Using Lasso regression algorithm, we filtered the significant signaling pathways enriched by GSEA, GSVA, or PPI analysis to develop a prognostic model. And a nomogram integrated with clinical factors and significant pathways was constructed to predict the precise probability of overall survival (OS) of BRCA patients in the TCGA dataset (n = 1,098) and another two testing sets (n = 415). Results. BRCA patients were stratified into the PC (n = 571) and GC (n = 527) subgroup with significantly different prognosis with 550 immune-related lncRNAs in the TCGA dataset. Integrated analysis revealed different immune response, oncogenic signaling, and metabolic reprograming pathways between these two subgroups. And a 5-pathway signature could predict the prognosis of BRCA patients between these two subgroups independently in the TCGA dataset, which was confirmed in another two cohorts from the GEO dataset. In the TCGA dataset, 5-year OS rate was 78% (95% CI: 73–84) vs. 82% (95% CI: 77–87) for the PC and GC group (HR = 1.63 (95% CI: 1.17–2.28), ). The predictive power was similar in another two testing sets (HR > 1.20, ). Finally, a nomogram is developed for clinical application, which integrated this signature and age to accurately predict the survival probability in BRCA patients. Conclusion. This 5-pathway signature correlated with immune-derived lncRNAs was able to precisely predict the prognosis for patients with BRCA and provided a rich source characterizing immune-related lncRNAs and further informed strategies to target BRCA vulnerabilities.

Motivation: Interactions among long noncoding RNAs, circular RNAs, microRNAs, and messenger RNAs form complex gene expression regulatory networks, which are of great significance for the diagnosis, prevention, and treatment of complex diseases. Although existing computational methods have been developed to predict interactions among certain molecular types, they are generally limited to single-modality perspectives, overlooking competitive specificity and co-target cooperativity across multi-omics molecules, and thereby limiting their ability to elucidate cross-omics regulatory mechanisms. Results: We proposed a novel cross-omics adaptive multimodal contrastive learning framework (MCOAN) that learns multimodal regulatory mechanisms and effectively predicts disease-associated molecular regulatory networks. Specifically, we first constructed a five-layer heterogeneous graph architecture to comprehensively integrate the complex regulatory associations among multi-omics nodes. Then, we proposed an unsupervised multimodal contrastive learning strategy that maximizes mutual information across distinct regulatory views, thereby enhancing node representations by efficiently capturing local neighborhood structure and global semantic information. Meanwhile, we also proposed a cross-omics adaptive learning mechanism that captures complex competitive specificity and co-target cooperativity across distinct regulatory networks, thereby further enhancing the structural awareness in node representations. Furthermore, we evaluated multiple downstream classifiers to accurately predict multimodal molecular regulatory networks. Finally, extensive experiments show that MCOAN consistently outperforms existing methods, achieving strong predictive accuracy and generalization (max AUC = 0.9881; max AUPR = 0.9826), and further confirm its real-world predictive performance through case studies. Availability and implementation: All resources are available at https://github.com/JunqiLab/MCOAN.git.

Background: In lung adenocarcinoma (LUAD), there remains a dearth of efficacious diagnostic studies including some inflammation-related genes to identify the LUAD subgroups with different clinical outcomes. Methods: First, two molecular subgroups were identified with mRNA expression profiling from The Cancer Genome Atlas (TCGA) by K-means algorithm. Gene set enrichment analysis (GSEA), immune infiltration, and Gene set variation analysis (GSVA) were applied to explore the biological functions between these two subtypes. Then, univariate and multivariate Cox regression analyses were selected to evaluate the independence of these subtypes in LUAD. Next, lasso regression was applied to identify the high-precision mRNAs to predict the subtype with favorable prognosis. Finally, a two-mRNA model was constructed using the method of multivariate Cox regression, and the effectiveness of the model was validated in a training set (n = 310) and three independent validation sets (n = 1. Results: Comprehensive genomic analysis was conducted of 310 LUAD samples and identified two subtypes associated with molecular classification and clinical prognosis: immune-enriched and non-immune-enriched subgroup. Then, a new model was developed based on two mRNAs (MS4A1 and MS4A2) in TCGA dataset and divided these LUAD patients into high-risk and low-risk subgroup with significantly different prognosis (HR = 1.644 (95% CI 1.153–2.342); p < 0.01), which was independence of the other clinical factors (p < 0.05). In addition, this new model had similar predictive effects in another three independent validation sets (HR > 1.445, p < 0.01). Conclusions: We constructed a robust model for predicting the risk of LUAD patients and evaluated the clinical outcomes independently with strong predictive power. This model stands as a reliable guide for implementing personalized treatment strategy.