Background: Breast cancer is the second largest cancer in the world, the incidence of breast cancer continues to rise worldwide, and women’s health is seriously threatened. Therefore, it is very important to explore the characteristic changes of breast cancer from the gene level, including the screening of differentially expressed genes and the identification of diagnostic markers.Methods: The gene expression profiles of breast cancer were obtained from the TCGA database. The edgeR R software package was used to screen the differentially expressed genes between breast cancer patients and normal samples. The function and pathway enrichment analysis of these genes revealed significant enrichment of functions and pathways. Next, download these pathways from KEGG website, extract the gene interaction relations, construct the KEGG pathway gene interaction network. The potential diagnostic markers of breast cancer were obtained by combining the differentially expressed genes with the key genes in the network. Finally, these markers were used to construct the diagnostic prediction model of breast cancer, and the predictive ability of the model and the diagnostic ability of the markers were verified by internal and external data.Results: 1060 differentially expressed genes were identified between breast cancer patients and normal controls. Enrichment analysis revealed 28 significantly enriched pathways (p < 0.05). They were downloaded from KEGG website, and the gene interaction relations were extracted to construct the gene interaction network of KEGG pathway, which contained 1277 nodes and 7345 edges. The key nodes with a degree greater than 30 were extracted from the network, containing 154 genes. These 154 key genes shared 23 genes with differentially expressed genes, which serve as potential diagnostic markers for breast cancer. The 23 genes were used as features to construct the SVM classification model, and the model had good predictive ability in both the training dataset and the validation dataset (AUC = 0.960 and 0.907, respectively).Conclusion: This study showed that the difference of gene expression level is important for the diagnosis of breast cancer, and identified 23 breast cancer diagnostic markers, which provides valuable information for clinical diagnosis and basic treatment experiments.

In cancer research, identifying cancer subtypes and evaluating prognosis are crucial for personalized diagnosis and treatment of cancer. With the advancement of high-throughput sequencing technologies, multi-omics data has become essential for cancer classification and prognostic analysis. By integrating deep learning techniques, it is possible to more accurately identify cancer subtypes, providing a robust basis for personalized treatment of cancer patients. In this study, we propose a convolutional autoencoder prognostic model incorporating a channel attention mechanism (CA-CAE). The model utilizes multi-omics data to predict survival-associated cancer subtypes and identify prognostic genes. We applied CA-CAE to multiple cancer types, successfully identifying subtypes in 15 distinct cancer types and revealing significant survival differences among these subtypes. Moreover, compared to traditional statistical methods and other deep learning approaches, CA-CAE demonstrated superior performance in predicting survival outcomes.

The promoter is an important noncoding DNA regulatory element, which combines with RNA polymerase to activate the expression of downstream genes. In industry, artificial arginine is mainly synthesized by Corynebacterium glutamicum. Replication of specific promoter regions can increase arginine production. Therefore, it is necessary to accurately locate the promoter in C. glutamicum. In the wet experiment, promoter identification depends on sigma factors and DNA splicing technology, this is a laborious job. To quickly and conveniently identify the promoters in C. glutamicum, we have developed a method based on novel feature representation and feature selection to complete this task, describing the DNA sequences through statistical parameters of multiple physicochemical properties, filtering redundant features by combining analysis of variance and hierarchical clustering, the prediction accuracy of the which is as high as 91.6%, the sensitivity of 91.9% can effectively identify promoters, and the specificity of 91.2% can accurately identify non-promoters. In addition, our model can correctly identify 181 promoters and 174 non-promoters among 400 independent samples, which proves that the developed prediction model has excellent robustness.

Intra-tumor molecular heterogeneity contributes substantially to survival differences among patients with breast cancer. Although deep learning-based multi-omics integration has shown promise for prognostic modeling, existing approaches may be affected by information leakage during model evaluation and often focus on either survival prediction or molecular subtype discovery. In this study, we developed the Deep Multi-modal Fusion Framework (DMFF), an integrated computational framework that supports continuous patient-level survival risk estimation and downstream de novo molecular subtype discovery within a unified analytical workflow. DMFF uses modality-specific denoising autoencoders to learn compact representations from mRNA and miRNA data, while a protein–protein interaction network-guided graph convolutional network is used to encode proteomic features. The resulting modality-specific representations are adaptively integrated through an attention-based fusion module. A Cox partial likelihood loss and a pairwise ranking loss are jointly optimized to generate continuous survival risk scores. To minimize information leakage, data imputation, standardization, feature preselection, hyperparameter optimization, and model checkpoint selection were performed within a nested five-fold cross-validation framework. Under nested cross-validation, the pure-omics DMFF model achieved a C-index of 0.656. After integrating the learned multi-omics representations with clinical characteristics using a Random Survival Forest, the joint model achieved a C-index of 0.706 and outperformed the evaluated baseline models in prognostic discrimination. Multivariable Cox regression further indicated that the DMFF-derived pure-omics risk score provided prognostic information independent of conventional clinical variables. Unsupervised clustering of the fused multi-omics representations identified three molecular subtypes with distinct survival outcomes, and repeated subsampling analysis supported the internal stability of the three-subtype solution. Tumor microenvironment analysis revealed distinct immune-cell enrichment patterns across the three subtypes. Comparison with PAM50 intrinsic subtypes suggested that the DMFF-derived and PAM50 classifications capture partially complementary aspects of breast cancer heterogeneity. Because the present study did not include immunotherapy exposure or treatment-response data, the potential relationship between the identified immune profiles and immunotherapy response should be regarded as exploratory and hypothesis-generating. Overall, DMFF provides a leakage-controlled framework for multi-omics survival prediction and downstream molecular subtype discovery, although its generalizability requires further validation in independent multi-center cohorts.