Bird's eye view analysis of in situ cholesterol metabolic pathways in breast cancer patients and its clinicopathological significance in their subtypes

A novel model based on lipid metabolism-related genes associated with immune microenvironment predicts metastasis of breast cancer

BACKGROUND

Breast cancer (BC) is the most prevalent malignant tumor among women worldwide and a significant cause of cancer-related deaths in females. Recent studies have shown that lipid metabolism-related genes (LMRGs) exhibit prognostic potential in various types of tumors, including BC. Our study aimed to establish a novel model to predict the metastasis of BC.

METHODS

Clinical information and corresponding RNA data of patients with BC were downloaded from The Cancer Genome Atlas and Gene Expression Omnibus databases. Consensus clustering was performed to identify novel molecular subgroups. Estimation of Stromal and Immune Cells in Malignant Tumor Tissues using Expression, microenvironment cell populations counter, microenvironment cell populations counter, and single-sample gene set enrichment analyses were employed to determine the tumor immune microenvironment and immune status of the identified subgroups. Functional analyses, including Gene Ontology and gene set enrichment analyses, were conducted to elucidate the underlying mechanisms. A prognostic risk model was constructed using the Least Absolute Shrinkage and Selection Operator algorithm and multivariate Cox regression analysis.

RESULTS

This study identified differential gene expression between patients with BC exhibiting metastasis and those without metastasis using public databases. Using the obtained data, we established predictive models based on six LMRGs. Furthermore, consensus clustering and prognostic score grouping analysis revealed that differentially expressed LMRGs influence tumor prognosis by regulating tumor immunity. To facilitate clinical application, we developed a nomogram integrating the risk model and clinical characteristics to accurately predict the prognosis of patients with BC.

CONCLUSION

We developed and validated a novel signature associated with LMRGs for predicting disease-free survival in patients with BC. The expression of LMRGs correlates with the immune microenvironment of patients with BC, providing new insights and improved strategies for the diagnosis and treatment of BC.

Significance of platelet adhesion-related genes in colon cancer based on non-negative matrix factorization-based clustering algorithm

Background

Although surgical methods are the most effective treatments for colon adenocarcinoma (COAD), the cure rates remain low, and recurrence rates remain high. Furthermore, platelet adhesion-related genes are gaining attention as potential regulators of tumorigenesis. Therefore, identifying the mechanisms responsible for the regulation of these genes in patients with COAD has become important. The present study aims to investigate the underlying mechanisms of platelet adhesion-related genes in COAD patients.

Methods

The present study was an experimental study. Initially, the effects of platelet number and related genomic alteration on survival were explored using real-world data and the cBioPortal database, respectively. Then, the differentially expressed platelet adhesion-related genes of COAD were analyzed using the TCGA database, and patients were further classified by employing the non-negative matrix factorization (NMF) analysis method. Afterward, some of the clinical and expression characteristics were analyzed between clusters. Finally, least absolute shrinkage and selection operator regression analysis was used to establish the prognostic nomogram. All data analyses were performed using the R package.

Results

High platelet counts are associated with worse survival in real-world patients, and alternations to platelet adhesion-related genes have resulted in poorer prognoses, based on online data. Based on platelet adhesion-related genes, patients with COAD were classified into two clusters by NMF-based clustering analysis. Cluster2 had a better overall survival, when compared to Cluster1. The gene copy number and enrichment analysis results revealed that two pathways were differentially enriched. In addition, the differentially expressed genes between these two clusters were enriched for POU6F1 in the transcription factor signaling pathway, and for MATN3 in the ceRNA network. Finally, a prognostic nomogram, which included the ALOX12 and ACTG1 genes, was established based on the platelet adhesion-related genes, with a concordance (C) index of 0.879 (0.848-0.910).

Conclusion

The mRNA expression-based NMF was used to reveal the potential role of platelet adhesion-related genes in COAD. The series of experiments revealed the feasibility of targeting platelet adhesion-associated gene therapy.

Germline and somatic genetic variability of oxysterol-related genes in breast cancer patients with early disease of the luminal subtype

Oxysterols, oxidized derivatives of cholesterol, have been implicated in multiple pathologies, including cancer. In breast cancer, the link is especially strong due to interactions between oxysterols and estrogen receptor activity. Here, we provide the first dedicated study of 113 oxysterol-related genes in breast cancer patients of the luminal subtype, in terms of both their somatic and germline variability, using targeted high-throughput DNA sequencing of 100 normal-tumor pairs with very high coverage. In the full cohort, or subsets of patients stratified by therapy, we found 12 germline variants in ABCA1, ABCA8, ABCC1, GPR183, LDLR, MBTPS1, NR1I2, OSBPL2, OSBPL3, and OSBPL5 to associate with poor survival of patients and variants in ABCA8, ABCG2, and HSD3B7 (three in total) associated with better survival. However, no associations remained significant after correction for multiple tests. Analysis of somatic variants revealed significantly (after FDR correction) poorer survival in patients mutated in CYP46A1 and 9 interacting (according to STRING analysis) genes, as well as in OSBPL3 and a set of 20 genes that collectively associated with the progesterone receptor status of patients. We propose further exploration of these genes in an integrative manner together with gene expression and epigenomic data.