Opposing roles of Nfkb2 gene products p100 and p52 in the regulation of breast cancer stem cells

p52 signaling promotes cellular senescence

Background

Nuclear factor-κB is a multi-subunit transcription factor that plays a central role in cellular senescence. We previously reported that an increase in the p52 subunit is seen in senescent cells and aged tissue. In the current work, we examined the mechanism by which p52 is activated and whether the increase in p52 promotes senescence.

Results

Using both primary mouse embryonic fibroblasts (MEFs) and WI-38 human lung fibroblasts, we examined cells after serial passage and following prolonged culture. An increase in p52 was found in the nucleus relative to pre-senescent cells. The increase in p52 protein was not reflected by an increase in NFKB2 mRNA or by an increase in the abundance of upstream activating kinases, IKKα and NIK. To examine whether p52 promotes senescence, we over-expressed mature p52 in primary MEFs. Significantly more senescence was seen compared to control, a finding not seen with p52 mutated at critical DNA binding residues. In addition, blocking p52 nuclear translocation with the peptide inhibitor, SN52, decreased β-galactosidase (β-gal) formation. Subsequent filtration studies demonstrated that proteins in conditioned media (CM) were necessary for the increase in p52 and mass spectrometry identified S100A4 and cyclophilin A (CYPA) as potential factors in CM necessary for induction of p52. The requirement of these proteins in CM for induction of p52 was confirmed using depletion and supplementation studies. In addition, we found that activation of STAT3 signaling was required for the increase in p52. Finally, genome wide ChIP-sequencing analysis confirmed that there is an increase in p52 chromatin enrichment with senescence and identified several downstream factors whose expression is regulated by increased p52 binding.

Conclusions

These results demonstrate that p52 nuclear translocation is increased in senescent cells by factors in conditioned media and that mature p52 induces cellular senescence. The data are consistent with the prior observation that p52 is elevated in aged tissue and support the hypothesis that p52 contributes to organismal aging.

Supplementary information

The online version contains supplementary material available at 10.1186/s13578-022-00779-6.

NMS-873 functions as a dual inhibitor of mitochondrial oxidative phosphorylation

Small-molecule inhibitors of enzyme function are critical tools for the study of cell biological processes and for treatment of human disease. Identifying inhibitors with suitable specificity and selectivity for single enzymes, however, remains a challenge. In this study we describe our serendipitous discovery that NMS-873, a compound that was previously identified as a highly selective allosteric inhibitor of the ATPase valosin-containing protein (VCP/p97), rapidly induces aerobic fermentation in cultured human and mouse cells. Our further investigation uncovered an unexpected off-target effect of NMS-873 on mitochondrial oxidative phosphorylation, specifically as a dual inhibitor of Complex I and ATP synthase. This work points to the need for caution regarding the interpretation of cell survival data associated with NMS-873 treatment and indicates that cellular toxicity associated with its use may be caused by both VCP/p97-dependent and VCP/p97-independent mechanisms.

Sensitivity to differential NRF1 gene signatures contributes to breast cancer disparities

PURPOSE

Nuclear respiratory factor 1 (NRF1) drives estrogen-dependent breast tumorigenesis. Herein we examined the impact of NRF1 activity on the aggressiveness and disparate molecular signature of breast cancer in Black, White, Asian, and Hispanic women.

METHODS

NRF1 activity by transcription factor target enrichment analysis and causal NRF1-target gene signatures by Bayesian Network Inference with Java Objects (BANJO) and Markov Chain Monte Carlo (MCMC)-based gene order were examined in The Cancer Genome Atlas (TCGA) breast cancer cohorts.

RESULTS

We are the first to report increased NRF1 activity based on its differential effects on genome-wide transcription associated with luminal A and B, HER2+ and triple-negative (TN) molecular subtypes of breast cancer in women of different race/ethnicity. We observed disparate NRF1 motif-containing causal gene signatures unique to Black, White, Asian, and Hispanic women for luminal A breast cancer. Further gene order searches showed molecular heterogeneity of each subtype of breast cancer. Six different gene order sequences involving CDK1, HMMR, CCNB2, CCNB1, E2F1, CREB3L4, GTSE1, and LMNB1 with almost equal weight predicted the probability of luminal A breast cancer in whites. Three different gene order sequences consisting of CCNB1 and GTSE1, and CCNB1, LMNB1, CDK1 or CASP3 predicted almost 100% probability of luminal B breast cancer in whites; CCNB1 and LMNB1 or GTSE predicted 100% HER2+ breast cancer in whites. GTSE1 and TUBA1C combined together predicted 100% probability of developing TNBC in whites; NRF1, TUBA1B and BAX with EFNA4, and NRF1 and BTRC predicated 100% TNBC in blacks. High expressor NRF1 TN breast tumors showed unfavorable prognosis with a high risk of breast cancer death in white women.

CONCLUSION

Our findings showed how sensitivity to high NRF1 transcriptional activity coupled with its target gene signatures contribute to racial differences in luminal A and TN breast cancer subtypes. This knowledge may be useful in personalized intervention to prevent and treat this clinically challenging problem.

Proteomic Discovery of Biomarkers to Predict Prognosis of High-Grade Serous Ovarian Carcinoma

Initial identification of biomarkers predicting the exact prognosis of high-grade serous ovarian carcinoma (HGSOC) is important in precision cancer medicine. This study aimed to investigate prognostic biomarkers of HGSOC through proteomic analysis. We conducted label-free liquid chromatography-mass spectrometry using chemotherapy-naïve, fresh-frozen primary HGSOC specimens, and compared the results between a favorable prognosis group (progression-free survival (PFS) ≥ 18 months, n = 6) and a poor prognosis group (PFS < 18 months, n = 6). Among 658 differentially expressed proteins, 288 proteins were upregulated in the favorable prognosis group and 370 proteins were upregulated in the poor prognosis group. Using hierarchical clustering, we selected α1-antitrypsin (AAT), nuclear factor-κB (NFKB), phosphomevalonate kinase (PMVK), vascular adhesion protein 1 (VAP1), fatty acid-binding protein 4 (FABP4), platelet factor 4 (PF4), apolipoprotein A1 (APOA1), and α1-acid glycoprotein (AGP) for further validation via immunohistochemical (IHC) staining in an independent set of chemotherapy-naïve primary HGSOC samples (n = 107). Survival analyses revealed that high expression of AAT, NFKB, and PMVK were independent biomarkers for favorable PFS. Conversely, high expression of VAP1, FABP4, and PF4 were identified as independent biomarkers for poor PFS. Furthermore, we constructed models predicting the 18-month PFS by combining clinical variables and IHC results. Through leave-one-out cross-validation, the optimal model was based on initial serum CA-125, germline BRCA1/2 mutations, residual tumors after surgery, International Federation of Gynecology and Obstetrics (FIGO) stage, and expression levels of the six proteins. The present results elucidate the proteomic landscape of HGSOC and six protein biomarkers to predict the prognosis of HGSOC.

Causal Inference Network of Genes Related with Bone Metastasis of Breast Cancer and Osteoblasts Using Causal Bayesian Networks

Background

The causal networks among genes that are commonly expressed in osteoblasts and during bone metastasis (BM) of breast cancer (BC) are not well understood. Here, we developed a machine learning method to obtain a plausible causal network of genes that are commonly expressed during BM and in osteoblasts in BC.

Methods

We selected BC genes that are commonly expressed during BM and in osteoblasts from the Gene Expression Omnibus database. Bayesian Network Inference with Java Objects (Banjo) was used to obtain the Bayesian network. Genes registered as BC related genes were included as candidate genes in the implementation of Banjo. Next, we obtained the Bayesian structure and assessed the prediction rate for BM, conditional independence among nodes, and causality among nodes. Furthermore, we reported the maximum relative risks (RRs) of combined gene expression of the genes in the model.

Results

We mechanistically identified 33 significantly related and plausibly involved genes in the development of BC BM. Further model evaluations showed that 16 genes were enough for a model to be statistically significant in terms of maximum likelihood of the causal Bayesian networks (CBNs) and for correct prediction of BM of BC. Maximum RRs of combined gene expression patterns showed that the expression levels of UBIAD1, HEBP1, BTNL8, TSPO, PSAT1, and ZFP36L2 significantly affected development of BM from BC.

Conclusions

The CBN structure can be used as a reasonable inference network for accurately predicting BM in BC.

Tudor-staphylococcal nuclease regulates the expression and biological function of alkylglycerone phosphate synthase via nuclear factor-κB and microRNA-127 in human glioma U87MG cells

Glioma is one of the malignant tumor types detrimental to human health; therefore, it is important to find novel targets and therapeutics for this tumor. The downregulated expression of Tudor-staphylococcal nuclease (SN) and alkylglycerone phosphate synthase (AGPS) can decrease cancer malignancy, and the overexpression of them can the increase viability and migration potential of various tumor cell types; however, the role of AGPS in the proliferation and migration of glioma, and the association of Tudor-SN and AGPS in human glioma is not clear. In the present study, it was determined that AGPS silencing suppressed the proliferation and migration potential of glioma U87MG cells, and suppressed the expression of the circular RNAs circ-ubiquitin-associated protein 2, circ-zinc finger protein 292 and circ-homeodomain-interacting protein kinase 3, and the long non-coding RNAs H19 imprinted maternally expressed transcript (non-protein coding), colon cancer-associated transcript 1 (non-protein coding) and hepatocellular carcinoma upregulated long non-coding RNA. Furthermore, Tudor-SN silencing suppressed the expression of AGPS; however, nuclear factor (NF)-κB and microRNA (miR)-127 retrieval experiments partially reduced the expression of AGPS. Additionally, it was determined that Tudor-SN silencing suppressed the activity of the mechanistic target of rapamycin (mTOR) signaling pathway, and NF-κB and miR-127 retrieval experiments partially reduced the activity of mTOR. Therefore, it was considered that NF-κB and miR-127 may be the mediators of Tudor-SN-regulated AGPS via the mTOR signaling pathway. These results improve on our knowledge of the mechanisms underlying Tudor-SN and AGPS in human glioma.

IΚΚε cooperates with either MEK or non-canonical NF-kB driving growth of triple-negative breast cancer cells in different contexts

BACKGROUND

Metastatic breast cancer carries a poor prognosis despite the success of newly targeted therapies. Treatment options remain especially limited for the subtype of triple negative breast cancer (TNBC). Several signaling pathways, including NF-κB, are altered in TNBC, and the complexity of this disease implies multi-faceted pathway interactions. Given that IKKε behaves as an oncogene in breast cancer, we hypothesized that IKKε regulates NF-κB signaling to control diverse oncogenic functions in TNBC.

METHODS

Vector expression and RNA interference were used to investigate the functional role of IKKε in triple-negative breast cancer cells. Viability, protein expression, NF-κB binding activity, invasion, anoikis, and spheroid formation were examined in cells expressing high or low levels of IKKε, in conjunction with p52 RNA interference or MEK inhibition.

RESULTS

This study found that non-canonical NF-κB p52 levels are inversely proportional to ΙΚΚε, and growth of TNBC cells in anchorage supportive, high-attachment conditions requires IKKε and activated MEK. Growth of these cells in anchorage resistant conditions requires IKKε and activated MEK or p52. In this model, IKKε and MEK cooperate to support overall viability whereas the p52 transcription factor is only required for viability in low attachment conditions, underscoring the contrasting roles of these proteins.

CONCLUSIONS

This study illustrates the diverse functions of IKKε in TNBC and highlights the adaptability of NF-κB signaling in maintaining cancer cell survival under different growth conditions. A better understanding of the diversity of NF-κB signaling may ultimately improve the development of novel therapeutic regimens for TNBC.

The crossroads of breast cancer progression: insights into the modulation of major signaling pathways

Cancer is the disease with highest public health impact in developed countries. Particularly, breast cancer has the highest incidence in women worldwide and the fifth highest mortality in the globe, imposing a significant social and economic burden to society. The disease has a complex heterogeneous etiology, being associated with several risk factors that range from lifestyle to age and family history. Breast cancer is usually classified according to the site of tumor occurrence and gene expression profiling. Although mutations in a few key genes, such as BRCA1 and BRCA2, are associated with high breast cancer risk, the large majority of breast cancer cases are related to mutated genes of low penetrance, which are frequently altered in the whole population. Therefore, understanding the molecular basis of breast cancer, including the several deregulated genes and related pathways linked to this pathology, is essential to ensure advances in early tumor detection and prevention. In this review, we outline key cellular pathways whose deregulation has been associated with breast cancer, leading to alterations in cell proliferation, apoptosis, and the delicate hormonal balance of breast tissue cells. Therefore, here we describe some potential breast cancer-related nodes and signaling concepts linked to the disease, which can be positively translated into novel therapeutic approaches and predictive biomarkers.