Risk stratification of triple-negative breast cancer with core gene signatures associated with chemoresponse and prognosis

Integrated bulk and single-cell RNA sequencing identifies an aneuploidy-based gene signature to predict sensitivity of lung adenocarcinoma to traditional chemotherapy drugs and patients' prognosis

Background

Patients with lung adenocarcinoma (LUAD) often develop a poor prognosis. Currently, researches on prognostic and immunotherapeutic capacity of aneuploidy-related genes in LUAD are limited.

Methods

Genes related to aneuploidy were screened based on bulk RNA sequencing data from public databases using Spearman method. Next, univariate Cox and Lasso regression analyses were performed to establish an aneuploidy-related riskscore (ARS) model. Results derived from bioinformatics analysis were further validated using cellular experiments. In addition, typical LUAD cells were identified by subtype clustering, followed by SCENIC and intercellular communication analyses. Finally, ESTIMATE, ssGSEA and CIBERSORT algorithms were employed to analyze the potential relationship between ARS and tumor immune environment.

Results

A five-gene ARS signature was developed. These genes were abnormally high-expressed in LUAD cell lines, and in particular the high expression of CKS1B promoted the proliferative, migratory and invasive phenotypes of LUAD cell lines. Low ARS group had longer overall survival time, higher degrees of inflammatory infiltration, and could benefit more from receiving immunotherapy. Patients in low ASR group responded more actively to traditional chemotherapy drugs (Erlotinib and Roscovitine). The scRNA-seq analysis annotated 17 cell subpopulations into seven cell clusters. Core transcription factors (TFs) such as CREB3L1 and CEBPD were enriched in high ARS cell group, while TFs such as BCLAF1 and UQCRB were enriched in low ARS cell group. CellChat analysis revealed that high ARS cell groups communicated with immune cells via SPP1 (ITGA4-ITGB1) and MK (MDK-NCl) signaling pathways.

Conclusion

In this research, integrative analysis based on the ARS model provided a potential direction for improving the diagnosis and treatment of LUAD.

Transcriptome Meta-Analysis of Triple-Negative Breast Cancer Response to Neoadjuvant Chemotherapy

Triple-negative breast cancer (TNBC) is a heterogeneous disease with varying responses to neoadjuvant chemotherapy (NAC). The identification of biomarkers to predict NAC response and inform personalized treatment strategies is essential. In this study, we conducted large-scale gene expression meta-analyses to identify genes associated with NAC response and survival outcomes. The results showed that immune, cell cycle/mitotic, and RNA splicing-related pathways were significantly associated with favorable clinical outcomes. Furthermore, we integrated and divided the gene association results from NAC response and survival outcomes into four quadrants, which provided more insights into potential NAC response mechanisms and biomarker discovery.

The Dual Role of C/EBPδ in Cancer

CCAAT/Enhancer-Binding Protein delta (C/EBPδ) is a transcription factor involved in differentiation and inflammation. While sparsely expressed in adult tissues, aberrant expression of C/EBPδ has been associated with different cancers. Initially, re-expression of C/EBPδ in cell cultures limited tumor cell proliferation, assigning it a tumor suppressor role. However, opposing observations were made in pre-clinical models and patients, suggesting that C/EBPδ not only mediates cell proliferation but dictates a broader spectrum of tumorigenesis-related effects. It is now widely accepted that C/EBPδ contributes to an inflammatory, tumor-promoting microenvironment, aids hypoxia adaption and contributes to the recruitment of blood vessels for improved nutrient supply to tumor cells and facilitated extravasation. This review summarizes the work published on this transcription factor in the field of cancer over the past decade. It points out areas in which a consensus on C/EBPδ's role appears to emerge and seek to explain seemingly contradictory results.

Transcriptomic profiles-based approach to decode the role of miR-122 in triple negative breast cancer

miR-122 has been considered both as tumor suppressor miRNA and oncomiR in breast tumor phenotypes. However, the role of miR-122 in triple-negative breast cancer (TNBC) is still unknown. In this study, the clinical value of miR-122 was used to describe the transcriptomic landscape of TNBC tumors obtained from The Cancer Genome Atlas database. Low expression levels of miR-122 were associated with poor overall survival (OS) of TNBC patients than those with higher expression levels of miR-122. We identified gene expression profiles in TNBC tumors expressed lower or higher miR-122. Gene coexpression networks analysis revealed gene modules and hub genes specific to TNBC tumors with low or high miR-122 levels. Gene ontology and KEGG pathways analysis revealed that gene modules in TNBC with gain of miR-122 were related to cell cycle and DNA repair, while in TNBC with loss of miR-122 were enriched in cell cycle, proliferation, apoptosis and activation of cell migration and invasion. The expression of hub genes distinguished TNBC tumors with gain or loss of miR-122 from normal breast tissues. Furthermore, high levels of hub genes were associated with better OS in TNBC patients. Interestingly, the gene coexpression network related to loss of miR-122 were enriched with target genes of miR-122, but this did not observed in those with gain of miR-122. Target genes of miR-122 are oncogenes mainly associated with cell differentiation-related processes. Finally, 75 genes were identified exclusively associated to loss of miR-122, which are also implicated in cell differentiation. In conclusion, miR-122 could act as tumor suppressor by controlling oncogenes in TNBC.

Systemically Identifying Triple-Negative Breast Cancer Subtype-Specific Prognosis Signatures, Based on Single-Cell RNA-Seq Data

Triple-negative breast cancer (TNBC) is a highly heterogeneous disease with different molecular subtypes. Although progress has been made, the identification of TNBC subtype-associated biomarkers is still hindered by traditional RNA-seq or array technologies, since bulk data detected by them usually have some non-disease tissue samples, or they are confined to measure the averaged properties of whole tissues. To overcome these constraints and discover TNBC subtype-specific prognosis signatures (TSPSigs), we proposed a single-cell RNA-seq-based bioinformatics approach for identifying TSPSigs. Notably, the TSPSigs we developed mostly were found to be disease-related and involved in cancer development through investigating their enrichment analysis results. In addition, the prognostic power of TSPSigs was successfully confirmed in four independent validation datasets. The multivariate analysis results showed that TSPSigs in two TNBC subtypes-BL1 and LAR, were two independent prognostic factors. Further, analysis results of the TNBC cell lines revealed that the TSPSigs expressions and drug sensitivities had significant associations. Based on the preceding data, we concluded that TSPSigs could be exploited as novel candidate prognostic markers for TNBC patients and applied to individualized treatment in the future.

Genome Instability-Derived Genes Are Novel Prognostic Biomarkers for Triple-Negative Breast Cancer

Background

Triple-negative breast cancer (TNBC) is an aggressive disease. Recent studies have identified genome instability-derived genes for patient outcomes. However, most of the studies mainly focused on only one or a few genome instability-related genes. Prognostic potential and clinical significance of genome instability-associated genes in TNBC have not been well explored.

Methods

In this study, we developed a computational approach to identify TNBC prognostic signature. It consisted of (1) using somatic mutations and copy number variations (CNVs) in TNBC to build a binary matrix and identifying the top and bottom 25% mutated samples, (2) comparing the gene expression between the top and bottom 25% samples to identify genome instability-related genes, and (3) performing univariate Cox proportional hazards regression analysis to identify survival-associated gene signature, and Kaplan–Meier, log-rank test, and multivariate Cox regression analyses to obtain overall survival (OS) information for TNBC outcome prediction.

Results

From the identified 111 genome instability-related genes, we extracted a genome instability-derived gene signature (GIGenSig) of 11 genes. Through survival analysis, we were able to classify TNBC cases into high- and low-risk groups by the signature in the training dataset (log-rank test p = 2.66e−04), validated its prognostic performance in the testing (log-rank test p = 2.45e−02) and Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) (log-rank test p = 2.57e−05) datasets, and further validated the predictive power of the signature in five independent datasets.

Conclusion

The identified novel signature provides a better understanding of genome instability in TNBC and can be applied as prognostic markers for clinical TNBC management.

Epigenetic Modulation of SPCA2 Reverses Epithelial to Mesenchymal Transition in Breast Cancer Cells

Simple Summary

The triple receptor negative breast cancer subtype, or TNBC, currently has no tailored treatment options. TNBC is highly metastatic, associated with high patient mortality, and disproportionately occurs in Black/African American women where it contributes to racial disparities in health outcomes. Therefore, we focused on new therapeutic approaches to TNBC. We discovered that levels of the Calcium-ATPase SPCA2 are abnormally low in TNBC and that these low levels correlate with poor survival prognosis in patients. Previously, we showed that recombinant SPCA2 prevented TNBC cells from acquiring aggressive “mesenchymal” properties associated with metastasis both in vitro and in vivo. These findings motivated us to search for drugs that turn the SPCA2 gene back on in TNBC cells. In this study, we show that histone deacetylase inhibitors increase SPCA2 levels, activate Ca²⁺ signaling and convert cancer cells to a less aggressive “epithelial” state. These findings could lead to new treatment options for TNBC.

Abstract

The secretory pathway Ca²⁺-ATPase SPCA2 is a tumor suppressor in triple receptor negative breast cancer (TNBC), a highly aggressive molecular subtype that lacks tailored treatment options. Low expression of SPCA2 in TNBC confers poor survival prognosis in patients. Previous work has established that re-introducing SPCA2 to TNBC cells restores basal Ca²⁺ signaling, represses mesenchymal gene expression, mitigates tumor migration in vitro and metastasis in vivo. In this study, we examined the effect of histone deacetylase inhibitors (HDACi) in TNBC cell lines. We show that the pan-HDACi vorinostat and the class I HDACi romidepsin induce dose-dependent upregulation of SPCA2 transcript with concurrent downregulation of mesenchymal markers and tumor cell migration characteristic of epithelial phenotype. Silencing SPCA2 abolished the ability of HDACi to reverse epithelial to mesenchymal transition (EMT). Independent of ATPase activity, SPCA2 elevated resting Ca²⁺ levels to activate downstream components of non-canonical Wnt/Ca²⁺ signaling. HDACi treatment led to SPCA2-dependent phosphorylation of CAMKII and β-catenin, turning Wnt signaling off. We conclude that SPCA2 mediates the efficacy of HDACi in reversing EMT in TNBC by a novel mode of non-canonical Wnt/Ca²⁺ signaling. Our findings provide incentive for screening epigenetic modulators that exploit Ca²⁺ signaling pathways to reverse EMT in breast tumors.

Coexpression Module Construction by Weighted Gene Coexpression Network Analysis and Identify Potential Prognostic Markers of Breast Cancer

Background: Breast cancer (BC) is a malignant tumor with the highest morbidity among women, disrupting millions of their lives worldwide each year. However, the molecular mechanisms underlying remain unclear. Methods: The RNA-Sequencing and clinical data of BC patients from The Cancer Genome Atlas (TCGA) database were analyzed by weighted gene coexpression network analysis (WGCNA). Additionally, coexpressed modules were used to detect their correlation with the clinical traits of BC. Next, nodes of the most significant coexpression modules were used for Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway, mRNA-lncRNA coexpression network and survival analyses. Results: In total, 2056 differentially expressed mRNAs (DEmRNAs) and 297 differentially expressed lncRNAs (DElncRNAs) were identified and subjected to WGCNA analysis, and 12 coexpression modules were generated. The top five significant modules (turquoise, green, red, brown, and blue modules) were related to one or more clinical traits of BC. In particular, the turquoise and green modules were chosen for further analysis. Next, by lncRNA-mRNA coexpression analysis of the turquoise and green modules, 12 DEmRNAs and 2 DElncRNAs were identified as hub nodes. The lncRNA-associated mRNAs of the networks were commonly related to several cancer-related pathways. Moreover, these networks also revealed central roles for RP11-389C8.2 and TGFBR2 in the turquoise module and MYLK, KIT, and RP11-394O4.5 in the green module. Furthermore, 16 DEmRNAs and 3 DElncRNAs in these two modules were significantly correlated with the overall survival of BC patients. Conclusions: The authors' study identified some prognostic biomarkers that might play important roles in the development and treatment of BC. In particular, lncRNAs AC016995.3, RP1-193H18.2, and RP11-166D19.1 were novel biomarkers for BC.