High CYP27A1 expression is a biomarker of favorable prognosis in premenopausal patients with estrogen receptor positive primary breast cancer

Associations between lifestyle, health, and clinical characteristics and circulating oxysterols and cholesterol precursors in women diagnosed with breast cancer: a cross-sectional study

Despite increasing evidence that cholesterol precursors and oxysterols, oxidized cholesterol metabolites, play a role in numerous pathological processes and diseases including breast cancer, little is known about correlates of these sterols in women with breast cancer. In this study, 2282 women with breast cancer and blood draw post diagnosis were included and cross-sectional associations between circulating levels of 15 sterols/oxysterols and (a) lifestyle, anthropometric, reproductive characteristics, (b) comorbidities and medication use, and (c) breast cancer tumor and treatment characteristics were calculated using generalized linear models. Obesity was strongly associated with circulating levels of 7-dehydrocholesterol (DC) (body mass index ≥ 30 vs. 18.5-24.9 kg/m2: 51.7% difference) and 7-ketocholesterol (KC) (40.0% difference). After adjustment for BMI, comorbidities such as cardiovascular disease were associated with higher levels of 7-DC (26.1% difference) and lower levels of desmosterol (- 16.4% difference). Breast cancer tumor characteristics including hormone receptor status, tumor stage, and endocrine therapy were associated with lanosterol, 24-DHLan, 7b-HC, and THC (e.g., THC; tumor stage IIIa vs. I: 36.9% difference). Weaker associations were observed for lifestyle characteristics and for any of the other oxysterols. The findings of this study suggest that cholesterol precursors are strongly associated with metabolic factors, while oxysterols are associated with breast cancer tumor characteristics, warranting further investigation into the role of cholesterol precursors and oxysterols in women with breast cancer and other populations.

27-Hydroxylation of oncosterone by CYP27A1 switches its activity from pro-tumor to anti-tumor

Oncosterone (6-oxo-cholestane-3β,5α-diol; OCDO) is an oncometabolite and a tumor promoter on estrogen receptor alpha-positive breast cancer (ER(+) BC) and triple-negative breast cancers (TN BC). OCDO is an oxysterol formed in three steps from cholesterol: 1) oxygen addition at the double bond to give α- or β- isomers of 5,6-epoxycholestanols (5,6-EC), 2) hydrolyses of the epoxide ring of 5,6-ECs to give cholestane-3β,5α,6β-triol (CT), and 3) oxidation of the C6 hydroxyl of CT to give OCDO. On the other hand, cholesterol can be hydroxylated by CYP27A1 at the ultimate methyl carbon of its side chain to give 27-hydroxycholesterol ((25R)-Cholest-5-ene-3beta,26-diol, 27HC), which is a tumor promoter for ER(+) BC. It is currently unknown whether OCDO and its precursors can be hydroxylated at position C27 by CYP27A1, as is the impact of such modification on the proliferation of ER(+) and TN BC cells. We investigated, herein, whether 27H-5,6-ECs ((25R)-5,6-epoxycholestan-3β,26-diol), 27H-CT ((25R)-cholestane-3β,5α,6β,26-tetrol) and 27H-OCDO ((25R)-cholestane-6-oxo-3β,5α,26-triol) exist as metabolites and can be produced by cells expressing CYP27A1. We report, for the first time, that these compounds exist as metabolites in humans. We give pharmacological and genetic evidence that CYP27A1 is responsible for their production. Importantly, we found that 27-hydroxy-OCDO (27H-OCDO) inhibits BC cell proliferation and blocks OCDO and 27-HC-induced proliferation in BC cells, showing that this metabolic conversion commutes the proliferative properties of OCDO into antiproliferative ones. These data suggest an unprecedented role of CYP27A1 in the control of breast carcinogenesis by inhibiting the tumor promoter activities of oncosterone and 27-HC.

Identification of prognostic biomarkers of breast cancer based on the immune-related gene module

Breast cancer (BC) is highly malignant and its mortality rate remains high. The development of immunotherapy has gradually improved the prognosis and survival rate of patients. Therefore, identifying molecular markers concerned with BC immunity is of great importance for the treatment of this disease. The Cancer Genome Atlas-breast invasive carcinoma (TCGA-BRCA) was utilized as the training set while the BC expression dataset from the gene expression omnibus database was taken as the validation set here. Weighted gene co-expression network analysis combined with Pearson analysis and Tumor immune estimation resource (TIMER) was used to obtain immune cell-related hub gene module. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed on this module. Then, receiver operating characteristic curves combining Kaplan-Meier was used to evaluate the effectiveness of the model. Feature genes were screened and the independence of risk score was evaluated by univariate and multivariate Cox analyses. Differences in immune characteristics were analyzed via single-sample gene set enrichment analysis and CIBERSORT, and differences in gene mutation frequency were assessed via GenVisR analysis. Finally, the expression levels of prognostic feature genes in BC cells were validated by quantitative reverse transcription polymerase chain reaction (qRT-PCR). In this study, cell immune-related gene modules in TCGA-BRCA were successfully excavated, and a five-gene (TNFRSF14, NFKBIA, DLG3, IRF2, and CYP27A1) prognostic model was established. The prognostic model could effectively forecast the prognosis and survival rate of BC patients. The result showed that human leukocyte antigen-related proteins and macrophage M2 scores were remarkably highly expressed in the high-risk group, whereas CD8+ T cells, natural killer cells, M1, and other anti-tumor cells were lowly expressed. The model could be used as an independent prognostic factor to predict the prognosis of BC patients. The results of qRT-PCR validation were consistent with the results in the database, that is, except DLG3, the other four feature genes were lowly expressed in BC. The five-gene model established in this study can predict the prognostic and immune mode of BC patients effectively, which is anticipated to become a feasible molecular target for BC therapy.

Epigenetic Dysregulation and Its Correlation with the Steroidogenic Machinery Impacting Breast Pathogenesis: Data Mining and Molecular Insights into Therapeutics

Breast cancer (BC) is a heterogeneous condition and comprises molecularly distinct subtypes. An imbalance in the levels of epigenetic histone deacetylases (HDACs), modulating estrogen accumulation, especially 17β-estradiol (E2), promotes breast tumorigenesis. In the present study, analyses of The Cancer Genome Atlas (TCGA) pan-cancer normalized RNA-Seq datasets revealed the dysregulation of 16 epigenetic enzymes (among a total of 18 members) in luminal BC subtypes, in comparison to their non-cancerous counterparts. Explicitly, genomic profiling of these epigenetic enzymes displayed increases in HDAC1, 2, 8, 10, 11, and Sirtuins (SIRTs) 6 and 7, and decreases in HDAC4-7, -9, and SIRT1-4 levels, respectively, in TCGA breast tumors. Kaplan-Meier plot analyses showed that these HDACs, with the exception of HDAC2 and SIRT2, were not correlated with the overall survival of BC patients. Additionally, disruption of the epigenetic signaling in TCGA BC subtypes, as assessed using both heatmaps and boxplots, was associated with the genomic expression of factors that are instrumental for cholesterol trafficking/utilization for accelerating estrogen/E2 levels, in which steroidogenic acute regulatory protein (STAR) mediates the rate-limiting step in steroid biosynthesis. TCGA breast samples showed diverse expression patterns of a variety of key steroidogenic markers and hormone receptors, including LIPE, CYP27A1, STAR, STARD3, CYP11A1, CYP19A1, ER, PGR, and ERBB2. Moreover, regulation of STAR-governed steroidogenic machinery was found to be influenced by various transcription factors, i.e., CREB1, CREM, SF1, NR4A1, CEBPB, SREBF1, SREBF2, SP1, FOS, JUN, NR0B1, and YY1. Along these lines, ingenuity pathway analysis (IPA) recognized a number of new targets and downstream effectors influencing BCs. Of note, genomic, epigenomic, transcriptional, and hormonal anomalies observed in human primary breast tumors were qualitatively similar in pertinent BC cell lines. These findings identify the functional correlation between dysregulated epigenetic enzymes and estrogen/E2 accumulation in human breast tumors, providing the molecular insights into more targeted therapeutic approaches involving the inhibition of HDACs for combating this life-threatening disease.

Transcriptomic analysis identifies CYP27A1 as a diagnostic marker for the prognosis and immunity in lung adenocarcinoma

BACKGROUND

The association between lipid metabolism disorder and carcinogenesis is well-established, but there is limited research on the connection between lipid metabolism-related genes (LRGs) and lung adenocarcinoma (LUAD). The objective of our research was to identify LRGs as the potential biomarkers for prognosis and assess their impact on immune cell infiltration in LUAD.

METHODS

We identified novel prognostic LRGs for LUAD patients via the bioinformatics analysis. CYP27A1 expression level was systematically evaluated via various databases, such as TCGA, UALCAN, and TIMER. Subsequently, LinkedOmics was utilized to perform the CYP27A1 co-expression network and GSEA. ssGSEA was conducted to assess the association between infiltration of immune cells and CYP27A1 expression. CYP27A1's expression level was validated by qRT-PCR analysis.

RESULTS

CYP27A1 expression was decreased in LUAD. Reduced CYP27A1 expression was linked to unfavorable prognosis in LUAD. Univariate and multivariate analyses indicated that CYP27A1 was an independent prognostic biomarker for LUAD patients. GSEA results revealed a positive correlation between CYP27A1 expression and immune-related pathways. Furthermore, CYP27A1 expression was positively correlated with the infiltration levels of most immune cells.

CONCLUSION

CYP27A1 is a potential biomarker for LUAD patients, and our findings provided a novel perspective to develop the prognostic marker for LUAD patients.

Endogenous estrogen receptor modulating oxysterols and breast cancer prognosis: Results from the MARIE patient cohort

BACKGROUND

27-hydroxycholesterol (HC) and 25-HC were identified as endogenous selective estrogen receptor modulators (SERMs) and estrogen receptor (ER) modulators, respectively. They are hypothesized to play a role in multiple physiologic processes and pathologies, including breast cancer development and progression.

METHODS

We evaluated circulating 27-HC and 25-HC, and outcomes following a breast cancer diagnosis in 2282 women from the MARIE study over median follow-up of 11.6 years. 27-HC and 25-HC were quantified by liquid chromatography-mass spectrometry. We calculated hazard ratios (HR) and 95% confidence intervals [CI] using multivariable Cox Proportional Hazards regression.

RESULTS

We observed no associations between 27-HC and breast cancer prognosis overall. Associations between 27-HC and survival differed by circulating estradiol concentrations and endocrine therapy, but not by hormone receptor status. Among women with estradiol levels below the median (0.08 nM), 27-HC was associated with higher risk of all-cause mortality (HRlog2 = 1.80 [1.20-2.71]) and breast cancer-specific mortality (HRlog2 = 1.95 [1.14-3.31]). No associations were observed in women with estradiol levels above the median. Higher 25-HC levels were associated with lower risk of recurrence (HRlog2 = 0.87 [0.77-0.98]).

CONCLUSION

Associations between 27-HC and breast cancer prognosis varied by circulating estradiol levels and endocrine therapy. Less consistent results were observed for 25-HC.

TUBB2B facilitates progression of hepatocellular carcinoma by regulating cholesterol metabolism through targeting HNF4A/CYP27A1

Cholesterol metabolism plays a critical role in the progression of hepatocellular carcinoma (HCC), but it is not clear how cholesterol metabolism is regulated. The tubulin beta class I genes (TUBBs) are associated with the prognosis of many different cancers. To confirm the function of TUBBs in HCC, the Kaplan-Meier method and Cox analyses were performed using TCGA and GSE14520 datasets. A higher expression of TUBB2B is an independent prognostic factor for shorter over survival in HCC patients. Deletion of TUBB2B in hepatocytes inhibits proliferation and promotes tumor cell apoptosis, while over-expression of TUBB2B has the opposite function. This result was confirmed in a mouse xenograft tumor model. Mechanistically, TUBB2B induces the expression of CYP27A1, an enzyme responsible for the conversion of cholesterol to 27-hydroxycholesterol, which leads to the up-regulation of cholesterol and the progression of HCC. In addition, TUBB2B regulates CYP27A1 via human hepatocyte nuclear factor 4alpha (HNF4A). These findings indicated that TUBB2B functions as an oncogene in HCC, and plays a role in promoting cell proliferation and anti-apoptosis through targeting HNF4A/CYP27A1/cholesterol.

Development and Validation of a Novel PPAR Signaling Pathway-Related Predictive Model to Predict Prognosis in Breast Cancer

This study is aimed at exploring the potential mechanism of the PPAR signaling pathway in breast cancer (BRCA) and constructing a novel prognostic-related risk model. We used various bioinformatics methods and databases to complete our exploration in this research. Based on TCGA database, we use multiple extension packages based on the R language for data conversion, processing, and statistics. We use LASSO regression analysis to establish a prognostic-related risk model in BRCA. And we combined the data of multiple online websites, including GEPIA, ImmuCellAI, TIMER, GDSC, and the Human Protein Atlas database to conduct a more in-depth exploration of the risk model. Based on the mRNA data in TCGA database, we conducted a preliminary screening of genes related to the PPAR signaling pathway through univariate Cox analysis, then used LASSO regression analysis to conduct a second screening, and successfully established a risk model consisting of ten genes in BRCA. The results of ROC curve analysis show that the risk model has good prediction accuracy. We can successfully divide breast cancer patients into high- and low-risk groups with significant prognostic differences (P = 1.92e − 05) based on this risk model. Combined with the clinical data in TCGA database, there is a correlation between the risk model and the patient's N, T, gender, and fustat. The results of multivariate Cox regression show that the risk score of this risk model can be used as an independent risk factor for BRCA patients. In particular, we draw a nomogram that can predict the 5-, 7-, and 10-year survival rates of BRCA patients. Subsequently, we conducted a series of pancancer analyses of CNV, SNV, OS, methylation, and immune infiltration for this risk model gene and used GDSC data to investigate drug sensitivity. Finally, to gain insight into the predictive value and protein expression of these risk model genes in breast cancer, we used GEO and HPA databases for validation. This study provides valuable clues for future research on the PPAR signaling pathway in BRCA.