Construction of Bone Metastasis-Specific Regulation Network Based on Prognostic Stemness-Related Signatures in Breast Invasive Carcinoma

Construction of a prognostic score model for breast cancer based on multi-omics analysis of study on bone metastasis

Background

Breast cancer (BRCA) is the most common type of cancer and the second leading cause of cancer-related death in women all over the world. Metastasis to bone is an indicator of poor prognosis in BRCA patients. This study aimed to develop a prognostic score model for predicting bone metastasis in patients with BRCA.

Methods

BRCA-related RNA sequencing datasets and corresponding clinical information were downloaded from the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA). Differentially expressed genes (DEGs) were screened using Limma package of R software. A risk score based predictive model was constructed based on the key genes identified through univariate Cox regression and the least absolute shrinkage and selection operator (LASSO) Cox regression. The gene expression profiles in BRCA patients were analyzed by gene set variation analysis (GSVA) and gene set enrichment analysis (GSEA). Random survival forest (RSF) analysis of BRCA patients with bone metastasis was conducted to identify the key DEGs.

Results

Based on DEG analysis, a total of 677 genes were identified as genes related to bone metastasis in BRCA. By univariate Cox regression and LASSO regression, 28 DEGs were identified as signature genes to develop the prognostic model. A risk score for each patient was created by incorporating the expression values of each specific gene and weighting them with the corresponding estimated regression coefficients. Patients were divided into a low-risk and a high-risk group based on the median risk score. Overall survival (OS) was significantly lower in the high-risk group. The receiver operating characteristic (ROC) curve and multi-omics analysis indicated that the model had high training/testing accuracy and a good clinical predictive value. We used extra data from GEO database to verify the robustness of the prognostic model, and the lower OS in high-risk group and area under the curve (AUC) value indicated the model had strong predictive efficacy for prognosis of BRCA.

Conclusions

A prognostic prediction model was constructed based on 28 key DEGs identified through multi-omics analysis of studies on bone metastasis. The model may provide a promising method for distinguishing the high-risk BRCA patients and help on decision making in addition to prognosis prediction for BRCA patients.

Endosialin in Cancer: Expression Patterns, Mechanistic Insights, and Therapeutic Approaches

Endosialin, also known as tumor endothelial marker 1 (TEM1) or CD248, is a single transmembrane glycoprotein with a C-type lectin-like domain. Endosialin is mainly expressed in the stroma, especially in cancer-associated fibroblasts and pericytes, in most solid tumors. Endosialin is also expressed in tumor cells of most sarcomas. Endosialin can promote tumor progression through different mechanisms, such as promoting tumor cell proliferation, adhesion and migration, stimulating tumor angiogenesis, and inducing an immunosuppressive tumor microenvironment. Thus, it is considered an ideal target for cancer treatment. Several endosialin-targeted antibodies and therapeutic strategies have been developed and have shown preliminary antitumor effects. Here, we reviewed the endosialin expression pattern in different cancer types, discussed the mechanisms by which endosialin promotes tumor progression, and summarized current therapeutic strategies targeting endosialin.

Novel insights from spatial transcriptome analysis in solid tumors

Since its first application in 2016, spatial transcriptomics has become a rapidly evolving technology in recent years. Spatial transcriptomics enables transcriptomic data to be acquired from intact tissue sections and provides spatial distribution information and remedies the disadvantage of single-cell RNA sequencing (scRNA-seq), whose data lack spatially resolved information. Presently, spatial transcriptomics has been widely applied to various tissue types, especially for the study of tumor heterogeneity. In this review, we provide a summary of the research progress in utilizing spatial transcriptomics to investigate tumor heterogeneity and the microenvironment with a focus on solid tumors. We summarize the research breakthroughs in various fields and perspectives due to the application of spatial transcriptomics, including cell clustering and interaction, cellular metabolism, gene expression, immune cell programs and combination with other techniques. As a combination of multiple transcriptomics, single-cell multiomics shows its superiority and validity in single-cell analysis. We also discuss the application prospect of single-cell multiomics, and we believe that with the progress of data integration from various transcriptomics, a multilayered subcellular landscape will be revealed.

Implications of Stemness Features in 1059 Hepatocellular Carcinoma Patients from Five Cohorts: Prognosis, Treatment Response, and Identification of Potential Compounds

Simple Summary

Cancer stemness has been reported to drive hepatocellular carcinoma (HCC) tumorigenesis and treatment resistance. However, comprehensive interpretations of transcriptomic stemness features in HCC patients have not been conducted in multiple cohorts. Our aim was to interpret clinical and therapeutic implications of transcriptional stemness features and explore potential compounds for HCC treatment. We found that transcriptional stemness indexes (mRNAsi) were independently associated with worse HCC prognosis. The HCC stemness risk model (HSRM) developed in this study significantly predicted prognosis and treatment response in various HCC cohorts. Analysis of two stemness subtypes suggested several liver-specific metabolic pathways, and mutations of TP53 and RB1 were associated with HCC transcriptional stemness. Moreover, we also identified potential compounds that target HCC transcriptional stemness. Our findings comprehensively characterized transcriptional stemness as a risk factor in HCC progression and treatment.

Abstract

Cancer stemness has been reported to drive hepatocellular carcinoma (HCC) tumorigenesis and treatment resistance. In this study, five HCC cohorts with 1059 patients were collected to calculate transcriptional stemness indexes (mRNAsi) by the one-class logistic regression machine learning algorithm. In the TCGA-LIHC cohort, we found mRNAsi was an independent prognostic factor, and 626 mRNAsi-related genes were identified by Spearman correlation analysis. The HCC stemness risk model (HSRM) was trained in the TCGA-LIHC cohort and significantly discriminated overall survival in four independent cohorts. HSRM was also significantly associated with transarterial chemoembolization treatment response and rapid tumor growth in HCC patients. Consensus clustering was conducted based on mRNAsi-related genes to divide 1059 patients into two stemness subtypes. On gene set variation analysis, samples of subtype I were found enriched with pathways such as DNA replication and cell cycle, while several liver-specific metabolic pathways were inhibited in these samples. Somatic mutation analysis revealed more frequent mutations of TP53 and RB1 in the subtype I samples. In silico analysis suggested topoisomerase, cyclin-dependent kinase, and histone deacetylase as potential targets to inhibit HCC stemness. In vitro assay showed two predicted compounds, Aminopurvalanol-a and NCH-51, effectively suppressed oncosphere formation and impaired viability of HCC cell lines, which may shed new light on HCC treatment.