RASAL1 and ROS1 Gene Variants in Hereditary Breast Cancer

The genetic duet of concurrent RASAL1 and PTEN alterations promotes cancer aggressiveness by cooperatively activating the PI3K-AKT pathway

The significance of the prominent tumor suppressor gene for RAS protein activator-like 1 (RASAL1) could be better understood by combined genetic, clinical, and functional studies. Here, we investigated the oncogenic and clinical impacts of genetic alterations of RASAL1, particularly when coexisting with genetic alterations of the gene for phosphatase and tensin homolog (PTEN), in 9924 cancers of 33 types in the TCGA database. We found common concurrent genetic alterations of the two genes, which were cooperatively associated with activation of the phosphatidylinositol 3-kinase (PI3K)-AKT pathway, with cancer progression and mortality rates being 46.36% and 31.72% with concurrent gene alterations, versus 29.80% and 16.93% with neither gene alteration (HR 1.64, 95% CI 1.46-1.84 and 1.77, 95% CI 1.53-2.05), respectively. This was enhanced by additional tumor protein p53 (TP53) gene alterations, with cancer progression and mortality rates being 47.65% and 34.46% with coexisting RASAL1, PTEN, and TP53 alterations versus 25.30% and 13.11% with no alteration (HR 2.21, 95% CI 1.92-2.56 and 2.76, 95% CI 2.31-3.30), respectively. In the case of breast cancer, this genetic trio was associated with a triple-negative risk of 68.75% versus 3.83% with no genetic alteration (RR 17.94, 95% CI 9.60-33.51), consistent with the aggressive nature of triple-negative breast cancer. Mice with double knockouts of Rasal1 and Pten displayed robust Pi3k pathway activation, with the development of metastasizing malignancies, while single gene knockout resulted in only benign neoplasma. These results suggest that RASAL1, like PTEN, is a critical player in negatively regulating the PI3K-AKT pathway; defect in RASAL1 causes RAS activation, thus initiating the PI3K-AKT pathway signaling, which cannot terminate with concurrent PTEN defects. Thus, the unique concurrent RASAL1 and PTEN defects drive oncogenesis and cancer aggressiveness by cooperatively activating the PI3K-AKT pathway. This represents a robust genetic mechanism to promote human cancer.

LCN2: Versatile players in breast cancer

Lipocalin 2 (LCN2) is a secreted glycoprotein that is produced by immune cells, including neutrophils and macrophages. It serves various functions such as transporting hydrophobic ligands across the cellular membrane, regulating immune responses, keeping iron balance, and fostering epithelial cell differentiation. LCN2 plays a crucial role in several physiological processes. LCN2 expression is upregulated in a variety of human diseases and cancers. High levels of LCN2 are specifically linked to breast cancer (BC) cell proliferation, apoptosis, invasion, migration, angiogenesis, immune regulation, chemotherapy resistance, and prognosis. As a result, LCN2 has gained attention as a potential therapeutic target for BC. This article offered an in-depth review of the advancement of LCN2 in the context of BC occurrence and development.

Single-cell combined with bulk-RNA data reveal a pattern related to angiogenesis in breast cancer patients: Individualized medicine

Angiogenesis contributes to tumor progression, aggressive behavior, and metastasis. Although several endothelial dysfunction genes (angiogenesis-related genes [ARGs]) have been identified as diagnostic biomarkers of breast cancer in a few studies, the mixed effects of ARGs have not been thoroughly investigated. The RNA sequencing data and patient survival datasets of breast cancer were obtained for further analysis. MSigDB website includes angiogenesis-related mechanisms. The consensus clustering analysis identifies 1082 breast cancer patients as three clusters. differential expression genes (DEGs) were identified by limma package. GO combined with gene set enrichment analysis (GSEA) to identify cytogenetic functions between two predefined clusters. Then Serpin Family F Member 1 (SERPINF1), angiomotin (AMOT), promyelocytic leukemia (PML), and BTG anti-proliferation factor 1 (BTG) were selected to construct prediction models using random forest survival analysis. External validation was performed using the GSE58812 triple-negative breast cancer cohort as the validation set. The median scoring system was used to discern the high- and low-risk groups, and there was a significant difference in their diagnostic results. Immunological infiltration scores were calculated using single sample gene set enrichment analysis (ssGSEA) and xCell algorithms, and consciousness scores were calculated using the R package "oncoPredict" for drugs in the Genomics of Drug Sensitivity in Cancer (GDSC) database. In addition, the single-cell analysis of seven triple-negative breast cancers using scRNA-seq information from GSE118389 demonstrated the interpretation of SERPINF1, AMOT, PML, and BTG1. In conclusion, this investigation engineered ARG-centric disease paradigms that not only prognosticated prospective therapeutic compounds, but also projected their mechanistic trajectories, thereby facilitating the proposition of tailored treatments within diverse patient cohorts diagnosed with breast cancer.