Construction of a prognostic 6-gene signature for breast cancer based on multi-omics and single-cell data

Single-Cell Sequencing: Genomic and Transcriptomic Approaches in Cancer Cell Biology

This article reviews the impact of single-cell sequencing (SCS) on cancer biology research. SCS has revolutionized our understanding of cancer and tumor heterogeneity, clonal evolution, and the complex interplay between cancer cells and tumor microenvironment. SCS provides high-resolution profiling of individual cells in genomic, transcriptomic, and epigenomic landscapes, facilitating the detection of rare mutations, the characterization of cellular diversity, and the integration of molecular data with phenotypic traits. The integration of SCS with multi-omics has provided a multidimensional view of cellular states and regulatory mechanisms in cancer, uncovering novel regulatory mechanisms and therapeutic targets. Advances in computational tools, artificial intelligence (AI), and machine learning have been crucial in interpreting the vast amounts of data generated, leading to the identification of new biomarkers and the development of predictive models for patient stratification. Furthermore, there have been emerging technologies such as spatial transcriptomics and in situ sequencing, which promise to further enhance our understanding of tumor microenvironment organization and cellular interactions. As SCS and its related technologies continue to advance, they are expected to drive significant advances in personalized cancer diagnostics, prognosis, and therapy, ultimately improving patient outcomes in the era of precision oncology.

Efferocytosis-related gene IL33 predicts prognosis and immune response and mediates proliferation and migration in vitro and in vivo of breast cancer

Background

Breast cancer (BRCA) has a high incidence among women, with poor prognosis and high mortality, which is increasing year by year. Efferocytosis is a process of phagocytosis of abnormal cells and is of great value in tumor research. Our study seeks to create a predictive model for BRCA using efferocytosis-related genes (ERGs) to explore the significance of efferocytosis in this disease.

Methods

In this research, Differential analysis, and univariate Cox regression were employed to identify genes linked to prognosis in BRCA patients. Then the BRCA patients were categorized into distinct groups using consensus clustering based on prognosis genes. Survival analysis, PCA, and t-SNE were performed to verify these groups. The enrichment of metabolic pathways within the detected clusters was evaluated using gene set variation analysis (GSVA) and gene set enrichment analysis (GSEA). Additionally, single-sample GSEA (ssGSEA) was used to examine changes in immune infiltration and enrichment. A risk prognostic model was constructed utilizing multivariable Cox regression and Least Absolute Shrinkage and Selection Operator (LASSO) analyses, and subsequently validated its predictive accuracy by stratifying patients according to the median risk score. Ultimately, some crucial independent prognostic genes were pinpointed and their expression, roles, and immune characteristics were explored in both laboratory and live models.

Results

Findings revealed 52 differentially expressed genes (DEGs), of which 21 were significantly linked to BRCA outcomes. These 21 genes were utilized for consensus clustering to categorize BRCA patients into two subtypes. Subtype B was linked to a worse prognosis compared to Subtype A, though both subtypes were distinguishable. The enriched pathways were mainly concentrated in Subtype A and were actively expressed in this group. Following this, a prognostic risk model was constructed using five risk genes, which was proven to possess significant predictive value. A significant link was identified between the immune microenvironment and the risk-associated genes and scores. IL33 was identified as an independent prognostic gene with important research value. Its in vivo expression results aligned with the data analysis findings, showing low expression in BRCA. Furthermore, overexpression of IL33 significantly inhibited BRCA growth and motility in vitro and in vivo, while also enhancing their vulnerability to destruction by activated CD8+ T cells.

Conclusion

The ERG-based risk model effectively predicts the prognosis of BRCA patients and shows a strong link with the immune microenvironment. IL33 stands out as a significant prognostic marker, crucial in the onset and advancement of BRCA. This highlights the necessity for additional studies and indicates that IL33 might be a potential target for BRCA treatment.

Ultrasound Genomics Reveals a Signature for Predicting Breast Cancer Prognosis and Therapy Response

Background: Breast cancer (BC) in women is the most common malignancy worldwide, but there is still a lack of validated tools to accurately assess patient prognosis and response to available chemotherapy treatment regimens. Method: We collected ultrasound images and transcriptome data of BC from our breast center and public database. Key ultrasound features were then identified by using the support vector machine (SVM) algorithm and correlated with prognostic genes. Long-term survival-related genes were identified through differential expression analysis, and a prognostic evaluation model was established by using Cox regression. In addition, VPS28 from the model was identified as a promising biomarker for BC. Results: Using univariate logistic regression and SVM algorithms, we identified 12 ultrasound features significantly associated with chemotherapy response. Subsequent correlation and differential expression analyses linked 401 genes to these features, from which five key signature genes were derived using Lasso and multivariate Cox regression models. This signature not only facilitates the stratification of patients into risk-specific treatment pathways but also predicts their chemotherapy response, thus supporting personalized medicine in clinical settings. Notably, VPS28, in the signature, emerged as a significant biomarker, strongly associated with poor prognosis, greater tumor invasiveness, and differing expression across demographic groups. Conclusion: In this study, we use ultrasound genomics to reveal a signature that can provide an effective tool for prognostic assessment and predicting chemotherapy response in patients with BC.

Bioinformatics analysis and validation of genes related to paclitaxel's anti-breast cancer effect through immunogenic cell death

Research indicated that Paclitaxel (PTX) can induce immunogenic cell death (ICD) through immunogenic modulation. However, the combination of PTX and ICD has not been extensively studied in breast cancer (BRCA). The TCGA-BRCA and GSE20685 datasets were enrolled in this study. Samples from the TCGA-BRCA dataset were consistently clustered based on selected immunogenic cell death-related genes (ICD-RGs). Next, candidate genes were obtained by overlapping differentially expressed genes (DEGs) between BRCA and normal groups, intersecting genes common to DEGs between cluster1 and cluster2 and hub module genes, and target genes of PTX from five databases. The univariate Cox algorithm and the least absolute shrinkage and selection operator (LASSO) were performed to obtain biomarkers and build a risk model. Following observing the immune microenvironment in differential risk subgroups, single-gene gene set enrichment analysis (GSEA) was carried out in all biomarkers. Finally, the expression of biomarkers was analyzed. Enrichment analysis showed that 626 intersecting genes were linked with inflammatory response. Further five biomarkers (CHI3L1, IL18, PAPLN, SH2D2A, and UBE2L6) were identified and a risk model was built. The model's performance was validated using GSE20685 dataset. Furthermore, the biomarkers were enriched with adaptive immune response. Lastly, the experimental results indicated that the alterations in IL18, SH2D2A, and CHI3L1 expression after treatment matched those in the public database. In this study, Five PTX-ICD-related biomarkers (CHI3L1, IL18, PAPLN, SH2D2A, and UBE2L6) were identified to aid in predicting BRCA treatment outcomes.