Identification of a novel glycolysis-related signature to predict the prognosis of patients with breast cancer

Bibliometric analysis of phosphoglycerate kinase 1 expression in breast cancer and its distinct upregulation in triple-negative breast cancer

BACKGROUND

Phosphoglycerate kinase 1 (PGK1) has been identified as a possible biomarker for breast cancer (BC) and may play a role in the development and advancement of triple-negative BC (TNBC).

AIM

To explore the PGK1 and BC research status and PGK1 expression and mechanism differences among TNBC, non-TNBC, and normal breast tissue.

METHODS

PGK1 and BC related literature was downloaded from Web of Science Core Collection Core Collection. Publication counts, key-word frequency, cooperation networks, and theme trends were analyzed. Normal breast, TNBC, and non-TNBC mRNA data were gathered, and differentially expressed genes obtained. Area under the summary receiver operating characteristic curves, sensitivity and specificity of PGK1 expression were determined. Kaplan Meier revealed PGK1's prognostic implication. PGK1 co-expressed genes were explored, and Gene Ontology, Kyoto Encyclopedia of Genes and Genomes, and Disease Ontology applied. Protein-protein interaction networks were constructed. Hub genes identified.

RESULTS

PGK1 and BC related publications have surged since 2020, with China leading the way. The most frequent keyword was "Expression". Collaborative networks were found among co-citations, countries, institutions, and authors. PGK1 expression and BC progression were research hotspots, and PGK1 expression and BC survival were research frontiers. In 16 TNBC vs non-cancerous breast and 15 TNBC vs non-TNBC datasets, PGK1 mRNA levels were higher in 1159 TNBC than 1205 non-cancerous breast cases [standardized mean differences (SMD): 0.85, 95% confidence interval (95%CI): 0.54-1.16, I² = 86%, P < 0.001]. PGK1 expression was higher in 1520 TNBC than 7072 non-TNBC cases (SMD: 0.25, 95%CI: 0.03-0.47, I² = 91%, P = 0.02). Recurrence free survival was lower in PGK1-high-expression than PGK1-low-expression group (hazard ratio: 1.282, P = 0.023). PGK1 co-expressed genes were concentrated in ATP metabolic process, HIF-1 signaling, and glycolysis/gluconeogenesis pathways.

CONCLUSION

PGK1 expression is a research hotspot and frontier direction in the BC field. PGK1 may play a strong role in promoting cancer in TNBC by mediating metabolism and HIF-1 signaling pathways.

A novel prognostic model of breast cancer based on cuproptosis-related lncRNAs

OBJECTIVE

Breast cancer (BC) is a deadly form of malignancy responsible for the death of a large number of women every year. Cuproptosis is a newly discovered form of cell death that may have implications for the prognosis of BC. Long non-coding RNAs (lncRNAs) have been shown to be involved in the progression and development of BC. Here within, a novel model capable of predicting the prognosis of patients with BC was established based on cuproptosis-related lncRNAs.

METHODS

Data of breast cancer patients was downloaded, including clinical information from The Cancer Genome Atlas (TCGA) database and lncRNAs related to cuproptosis were isolated. In total, nine lncRNAs related to copper death were obtained by Cox regression model based on Least Absolute Shrinkage and Selector Operation (LASSO) algorithm for model construction. The model was verified by overall survival (OS), progression-free survival (PFS) and receiver operating characteristic (ROC) curve. The differences in immune function, tumor mutation burden (TMB) and tumor immune dysfunction and exclusion (TIDE) between patients with different risk scores were analyzed.

RESULTS

Based on cuproptosis-related lncRNAs, a prognostic model for predicting BC was constructed. Each patient was assigned a risk score based on our model formula. We found that patients with higher risk scores had significantly lower OS and PFS, increased TMB, and higher sensitivity to immunotherapy.

CONCLUSIONS

The model established in this study based on cuproptosis-related lncRNAs may be capable of improving the OS of patients with BC.

A novel PD-1/PD-L1 pathway molecular typing-related signature for predicting prognosis and the tumor microenvironment in breast cancer

BACKGROUND

Currently, the development of breast cancer immunotherapy based on the PD-1/PD-L1 pathway is relatively slow, and the specific mechanism affecting the immunotherapy efficacy in breast cancer is still unclear.

METHODS

Weighted correlation network analysis (WGCNA) and the negative matrix factorization (NMF) were used to distinguish subtypes related to the PD-1/PD-L1 pathway in breast cancer. Then univariate Cox, least absolute shrinkage and selection operator (LASSO), and multivariate Cox regression were used to construct the prognostic signature. A nomogram was established based on the signature. The relationship between the signature gene IFNG and breast cancer tumor microenvironment was analyzed.

RESULTS

Four PD-1/PD-L1 pathway-related subtypes were distinguished. A prognostic signature related to PD-1/PD-L1 pathway typing was constructed to evaluate breast cancer's clinical characteristics and tumor microenvironment. The nomogram based on the RiskScore could be used to accurately predict breast cancer patients' 1-year, 3-year, and 5-year survival probability. The expression of IFNG was positively correlated with CD8+ T cell infiltration in the breast cancer tumor microenvironment.

CONCLUSION

A prognostic signature is constructed based on the PD-1/PD-L1 pathway typing in breast cancer, which can guide the precise treatment of breast cancer. The signature gene IFNG is positively related to CD8+ T cell infiltration in breast cancer.

Comprehensive evaluation of breast cancer immunotherapy and tumor microenvironment characterization based on interleukin genes-related risk model

Breast cancer (BRCA) is the most prevalent malignancy and the leading cause of death in women. Interleukin (IL) genes are critical in tumor initiation and control. Nevertheless, the prognosis value of the IL in BRCA remains unclear. We collected data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO), and 94 IL genes were identified from GeneCard. Based on the random forest (RF), least absolute shrinkage and selection operator (LASSO) analysis, and multivariate Cox regression analysis, we constructed an IL signature. GSE22219, GSE25065, and GSE21653 were derived as validation sets. The expression differences in the tumor microenvironment (TME), immunotherapy, and chemosensitivity of BRCA between the high- and low-risk groups were evaluated. Overall, 21 IL genes were selected to construct an IL risk model, of which IL18BP, IL17D, and IL23A were the first time identified as prognostic genes in BRCA. IL score could distinguish BRCA patients with inferior outcomes, and AUC of it was 0.70, 0.76, and 0.72 for 1-,3- and 5- years, respectively, which was also verified in GSE22219, GSE25065, and GSE21653 cohorts. Meanwhile, compared to luminal A and luminal B, HER2-positive and TNBC had significantly higher IL score. Besides, the high-risk group had a significantly higher prevalence of TP53 and TTN but a lower prevalence of PIK3CA, as well as higher tumor mutation burden (TMB) and neoantigen level. High- and low-risk groups exhibited notable differences in immunomodulators and tumor infiltrates immune cells (TIICs), and the high-risk group had significantly lower Tumor Immune Dysfunction and Exclusion (TIDE) score. Additionally, the high-risk group has more responders to immune or anti-HER2 combination therapy, whereas the low-risk group has higher sensitivity to docetaxel and paclitaxel. Consequently, we constructed a reliable risk model based on the IL genes, which can provide more information on both the risk stratification and personalizing management strategies for BRCA.

LINC00092 Modulates Oxidative Stress and Glycolysis of Breast Cancer Cells via Pyruvate Carboxylase-Mediated AKT/mTOR Pathway

Background

The discovery of noncoding RNAs (ncRNAs) offers new options for cancer-targeted therapy. This study is aimed at exploring the regulatory function of LINC00092 on breast cancer (BC) oxidative stress and glycolysis, along with internal mechanism concerning pyruvate carboxylase (PC).

Methods

Bioinformatics analysis was used to explore LINC00092 (or friend leukemia virus integration 1 (FLI1)) expression on BC progression, as well as oxidative stress and glycolysis in BC. After LINC00092 overexpression or silence, BC cell viability, proliferation, migration, invasion, oxidative stress, glycolysis, and AKT/mTOR pathway were detected. Following 2-DG, SC79, or MK2206 treatment, effects of LINC00092 on BC cells were measured. Moreover, regulatory activity of LINC00092 in PC expression was analyzed. Whether PC participated in the modulation of LINC00092 on BC cell functions was explored.

Results

LINC00092 was lowly expressed in BC and negatively related to BC progression. FLI1 bound to LINC00092 promoter to positively modulate LINC00092. LINC00092 overexpression inhibited BC cell proliferation, migration, invasion, oxidative stress, glycolysis, and AKT/mTOR pathway and likewise suppressed BC growth in vivo. Silence of LINC00092 had opposite influences. 2-DG partially reversed the LINC00092 silence-resulted increase of BC cell proliferation. SC79 alleviated the function of LINC00092 overexpression on BC cell functions. MK2206 had the contrary influence of SC79. Besides, LINC00092 bound to PC to modulate ubiquitination degradation of PC protein. PC took part in the influences of LINC00092 on BC cell functions.

Conclusions

LINC0092 modulates oxidative stress and glycolysis of BC cells via the PC-mediated AKT/mTOR pathway, which is possibly a target for BC diagnosis and therapy.

Screening potential immune signatures for early-stage basal-like/triple-negative breast cancer

Background

Breast cancer (BC) is a highly heterogeneous disease. Among the BC molecular subtypes, basal-like/triple-negative BC (TNBC) is characterized by a high propensity for relatively early metastases and a lack of available endocrine and targeted therapies. Therefore, this study aimed to discover potential signatures for predicting the immune response in early-stage basal-like/triple-negative BC.

Method

A total of 86 cases of early-stage TNBC from the TCGA and 459 cases of normal breast tissue from GTEx were enrolled and analyzed to screen out differentially expressed genes (DEGs). Then, the prognostic effect and tumor immune cell infiltration relationship with the basal-like-specific DEGs were also evaluated.

Results

A total of 1556 DEGs, including 929 upregulated genes and 627 downregulated genes, were screened in early-stage basal-like BC. Two prognosis-associated DEGs, GAL and TTC36, were finally found to be basal-like BC specific. However, only GAL was significantly correlated with tumor immune-infiltrating cells, especially CD8⁺ T cells. The expressions of GAL and TTC36 were revalidated by using the GEO dataset.

Conclusion

GAL might be an immune signature for the response to immune checkpoint therapy in early basal-like/triple-negative BC.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12957-022-02683-2.

Analysis of Tumor Glycosylation Characteristics and Implications for Immune Checkpoint Inhibitor’s Efficacy for Breast Cancer

The alterations of glycosylation, which is a common post-translational modification of proteins, have been acknowledged as key events in breast cancer (BC) oncogenesis and progression. The aberrant expression of glycosyltransferases leads to aberrant glycosylation patterns, posing the diagnostic potential in BC outcomes. The present study aims to establish a glycosyltransferase-based signature to predict BC prognosis and response to immune checkpoint inhibitors. We firstly screened 9 glycosyltransferase genes from The Cancer Genome Atlas (TCGA) database and accordingly established a glyco-signature for predicting the prognosis in BC patients. Patients with BC were successfully divided into high-risk and low-risk groups based on the median cutoff point for risk scores in this signature. Next, the combinational analyses of univariate and multivariate Cox regression, Kaplan–Meier, and receiver operating characteristic (ROC) curves were used to prove that this glyco-signature possessed excellent predictive performance for prognosis of BC patients, as the high-risk group possessed worse outcomes, in comparison to the low-risk group. Additionally, the Gene Set Enrichment Analysis (GSEA) and immunologic infiltration analysis were adopted and indicated that there was a more immunosuppressive state in the high-risk group than that in the low-risk group. The clinical sample validation verified that glycosyltransferase genes were differentially expressed in patients in the low- and high-risk groups, while the biomarkers of antitumor M1 macrophages were increased and N-glycosyltransferase STT3A decreased in the low-risk group. The final in vitro assay showed that the silencing of STT3A suppressed the proliferation and migration of BC cells. Collectively, our well-constructed glyco-signature is able to distinguish the high- and low-risk groups and accordingly predict BC prognosis, which will synergistically promote the prognosis evaluation and provide new immunotherapeutic targets for combating BC.