STARD4 promotes breast cancer cell malignancy

Comprehensive characterization of stemness-related lncRNAs in triple-negative breast cancer identified a novel prognostic signature related to treatment outcomes, immune landscape analysis and therapeutic guidance: a silico analysis with in vivo experiments

BACKGROUND

Cancer stem cells (CSCs) and long non-coding RNAs (lncRNAs) are known to play a crucial role in the growth, migration, recurrence, and drug resistance of tumor cells, particularly in triple-negative breast cancer (TNBC). This study aims to investigate stemness-related lncRNAs (SRlncRNAs) as potential prognostic indicators for TNBC patients.

METHODS

Utilizing RNA sequencing data and corresponding clinical information from the TCGA database, and employing Weighted Gene Co-expression Network Analysis (WGCNA) on TNBC mRNAsi sourced from an online database, stemness-related genes (SRGs) and SRlncRNAs were identified. A prognostic model was developed using univariate Cox and LASSO-Cox analysis based on SRlncRNAs. The performance of the model was evaluated using Kaplan-Meier analysis, ROC curves, and ROC-AUC. Additionally, the study delved into the underlying signaling pathways and immune status associated with the divergent prognoses of TNBC patients.

RESULTS

The research identified a signature of six SRlncRNAs (AC245100.6, LINC02511, AC092431.1, FRGCA, EMSLR, and MIR193BHG) for TNBC. Risk scores derived from this signature were found to correlate with the abundance of plasma cells. Furthermore, the nominated chemotherapy drugs for TNBC exhibited considerable variability between different risk score groups. RT-qPCR validation confirmed abnormal expression patterns of these SRlncRNAs in TNBC stem cells, affirming the potential of the SRlncRNAs signature as a prognostic biomarker.

CONCLUSION

The identified signature not only demonstrates predictive power in terms of patient outcomes but also provides insights into the underlying biology, signaling pathways, and immune status associated with TNBC prognosis. The findings suggest the possibility of guiding personalized treatments, including immune checkpoint gene therapy and chemotherapy strategies, based on the risk scores derived from the SRlncRNA signature. Overall, this research contributes valuable knowledge towards advancing precision medicine in the context of TNBC.

A signature based on circadian rhythm-associated genes for the evaluation of prognosis and the tumour microenvironment in HNSCC

The morbidity and mortality rates of head and neck squamous cell carcinoma (HNSCC) remain high worldwide. Therefore, there is an urgent need to identify a new prognostic biomarker to guide the personalized treatment of HNSCC patients. Increasing evidence suggests that circadian rhythm genes play an important role in the development and progression of cancer. We aimed to explore the value of circadian rhythm genes in predicting prognosis and guiding the treatment of HNSCC. We first obtained a list of circadian rhythm genes from previous research. The sequencing data were retrieved from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Finally, univariate Cox proportional hazard analysis, least absolute shrinkage and selection operator (LASSO) regression, and multivariate Cox proportional hazard analysis were performed to develop a prognostic signature (Circadian Rhythm-Related Gene Prognostic Index, CRRGPI) consisting of nine circadian rhythm genes. The signature exhibited good performance in predicting overall survival. Patients with low CRRGPI scores had lower metabolic activities and an active antitumour immunity ability. Additionally, a clinical cohort was used to further evaluate the ability of the CRRGPI to predict the efficacy of immune checkpoint inhibitors. In conclusion, the novel circadian rhythm-related gene signature can provide a precise prognostic evaluation with the potential capacity to guide individualized treatment regimens for HNSCC patients.

Inhibiting KLRB1 expression is associated with impairing cancer immunity and leading to cancer progression and poor prognosis in breast invasive carcinoma patients

BACKGROUND

The association between Killer cell lectin like receptor B1 (KLRB1) and cancer has been reported, but the roles of KLRB1 in breast invasive carcinoma (BRCA) has not been fully revealed.

METHODS

Our study utilized the Cancer Genome Atlas (TCGA), Kaplan-Meier (K-M) Plotter, and TIMER databases to investigate the expression and clinical relevance of KLRB1 in BRCA and to explore its roles and mechanism in BRCA progression using gene set enrichment analysis, CCK-8, migration, apoptosis, and western blotting. We examined the relationship between KLRB1 expression and the BRCA immune microenvironment, using data from TCGA, and Gene Expression Profiling Interactive Analysis (GEPIA) databases and validated these findings in K-M Plotter databases.

RESULTS

A significant decrease of KLRB1 expression was observed in BRCA patients. BRCA patients with low KLRB1 levels were associated with older age, advanced disease stage, HER2-positivity, poor prognosis, and a decreased survival probability compared to the high-expression group. Increased KLRB1 expression levels were correlated with inhibition of breast cancer cell proliferation, migration, and invasion, as well as promotion of cell apoptosis, possible through regulation of the NF-κB, PI3K/AKT, and TNF signaling pathways. Moreover, the study also indicated that decreased KLRB1 expression correlated with tumor purity, immune score, and immune cell infiltration (B cells, CD8+ T cells, CD4+ T cells, neutrophils, dendritic cells, among others), cell markers, and immunotherapy.

CONCLUSION

Decreased KLRB1 expression in BRCA is associated with poor prognosis and immune microenvironment. This study also highlights KLRB1 as a potential molecular marker for poor prognosis in BRCA patients, and therefore, it may provide clinical implications for the management of patients with BRCA.

Correlation between serum lipid levels and endocrine resistance in patients with ER-positive breast cancer

Lipid metabolism may be involved in the development of endocrine drug resistance in ER-positive (ER+) breast cancer (BC). This study aimed to investigate the relationship between serum lipid levels, risk stratification of dyslipidemia, and endocrine resistance. We collected the data from 166 ER + breast cancer patients who received endocrine therapy (ET). 73 of 166 patients (44.0%)developed endocrine resistance. Univariate and multivariate COX regression were conducted to explore the potential factors affecting endocrine resistance in BC. The clinical T stage, mean serum lipid levels in ET progression-free-survival (total cholesterol, triglycerides, low-density lipoprotein cholesterol, apolipoprotein A, and triglycerides/high-density lipoprotein cholesterol) were correlated with endocrine resistance (R = 0.214, P = .006; R = 0.268, P < .001; R = 0.182, P = .019;R = 0.197, P = .011; R = 0.211, P = .006; R = 0.159, P < .041). Clinical stage, triglycerides (TG) in endocrine therapy progression-free-survival (ePFS) and low-density lipoprotein cholesterol (LDL-C) in ePFS were independent predictors of endocrine resistance (P < .05; OR = 1.406, CI 1.108-1.783, P < .05; OR = 1.309, CI 1.026-1.669, P < .05, respectively). Moreover, in clinical stage III, the ePFS was worse in patients with in the high-risk and extremely high-risk group the median ePFS time was 8.0 months (95% CI: 1.140-14.860, P < .05). Clinical stage, TG in ePFS and LDL-C in ePFS may act as a new predictive biomarker for endocrine resistance in BC. The lipid levels of BC patients should be closely monitored throughout the treatment process, and patients with dyslipidemia should receive treatment immediately.

Correlation analysis of lipid metabolism genes with the immune microenvironment in gastric cancer and the construction of a novel gene signature

BACKGROUND

Lipid metabolism reprogramming plays an important role in cell growth, proliferation, angiogenesis and invasion of cancer. However, the prognostic value of lipid metabolism during gastric cancer (GC) progression and the relationship with the immune microenvironment are still unclear. The aim of this study was to clarify the correlation between lipid metabolism genes and GC immunity.

METHOD

We obtained 350 patients from The Cancer Genome Atlas (TCGA) and 355 patients from Gene Expression Omnibus (GEO) databases. Lipid metabolism-related gene datasets were obtained from the Reactome and Kyoto Encyclopedia of Genes and Genomes (KEGG) databases. Molecular subtypes were obtained by Consensus clustering, and subtype immune status was analyzed using ESTIMATE, TIMER and microenvironmental cell population counter (MCP Counter) algorithm for immune analysis. Functional analyses included the application of Gene Set Enrichment Analysis (GSEA), KEGG, gene ontology (GO), and Protein-Protein Interaction Networks (PPI) to evaluate the molecular mechanisms of different subtypes. Weighted gene co-expression network analysis (WGCNA) was used to identify genes associated with immunity. The LASSO algorithm and multivariate Cox regression analysis were used to construct prognostic risk models.

RESULT

Based on the lipid metabolism genes found in GC, patients with GC can be divided into two subgroups with significantly different survival. The subgroup with a better prognosis presented higher immune scores and immune infiltrating cell abundance. 1170 immune-related genes were screened by WGCNA, and further screening by PPI network analysis revealed that PTPRC, CD4, ITGB2 and LCP2 were closely associated with immune cells. Combined with the TIDE score results, it was found that the population with high expression of the above genes might be more sensitive to immunotherapy. In addition, a survival prediction model for GC was developed based on five survival-related lipid metabolism genes, PIAS4, PLA2R1, PRKACA, SLCO1A2 and STARD4. The ROC analysis over time showed that the risk prediction score model had good stability.

CONCLUSION

Lipid metabolism gene expression is correlated with the immune microenvironment in GC patients and can accurately predict their prognosis. Studies on lipid metabolism and GC immunity can help to screen the population for immunotherapy benefits.

SREBF2-STARD4 axis confers sorafenib resistance in hepatocellular carcinoma by regulating mitochondrial cholesterol homeostasis

Sorafenib resistance limits its survival benefit for treatment of hepatocellular carcinoma (HCC). Cholesterol metabolism is dysregulated in HCC, and its role in sorafenib resistance of HCC has not been fully elucidated. Aiming to elucidate this, in vitro and in vivo sorafenib resistant models were established. Sterol regulatory element binding transcription factor 2 (SREBF2), the key regulator of cholesterol metabolism, was activated in sorafenib resistant HepG2 and Huh7 cells. Knockdown of SREBF2 resensitized sorafenib resistant cells and xenografts tumors to sorafenib. Further study showed that SREBF2 positively correlated with StAR related lipid transfer domain containing 4 (STARD4) in our sorafenib resistant models and publicly available datasets. STARD4, mediating cholesterol trafficking, not only promoted proliferation and migration of HepG2 and Huh7 cells, but also increased sorafenib resistance in liver cancer. Mechanically, SREBF2 promoted expression of STARD4 by directly binding to its promoter region, leading to increased mitochondrial cholesterol levels and inhibition of mitochondrial cytochrome c release. Importantly, knockdown of SREBF2 or STARD4 decreased mitochondrial cholesterol levels and increased mitochondrial cytochrome c release, respectively. Moreover, overexpression of STARD4 reversed the effect of SREBF2 knockdown on mitochondrial cytochrome c release and sorafenib resistance. In conclusion, SREBF2 promotes STARD4 transcription, which in turn contributes to mitochondrial cholesterol transport and sorafenib resistance in HCC. Therefore, targeting the SREBF2-STARD4 axis would be beneficial to a subset of HCC patients with sorafenib resistance.

STARD3: A Prospective Target for Cancer Therapy

Cancer is one of the major causes of death in developed countries and current therapies are based on surgery, chemotherapeutic agents, and radiation. To overcome side effects induced by chemo- and radiotherapy, in recent decades, targeted therapies have been proposed in second and even first lines. Targeted drugs act on the essential pathways involved in tumor induction, progression, and metastasis, basically all the hallmark of cancers. Among emerging pathways, the cholesterol metabolic pathway is a strong candidate for this purpose. Cancer cells have an accelerated metabolic rate and require a continuous supply of cholesterol for cell division and membrane renewal. Steroidogenic acute regulatory related lipid transfer (START) proteins are a family of proteins involved in the transfer of lipids and some of them are important in non-vesicular cholesterol transportation within the cell. The alteration of their expression levels is implicated in several diseases, including cancers. In this review, we report the latest discoveries on StAR-related lipid transfer protein domain 3 (STARD3), a member of the START family, which has a potential role in cancer, focusing on the structural and biochemical characteristics and mechanisms that regulate its activity. The role of the STARD3 protein as a molecular target for the development of cancer therapies is also discussed. As STARD3 is a key protein in the cholesterol movement in cancer cells, it is of interest to identify inhibitors able to block its activity.

Expression of lipid transport-associated genes in lipid-deprived cancer cells

Cancer cells are known to significantly alter their lipid profiles in response to changes in extracellular lipid availability. Recent studies have shown that in response to lipid deprivation, cancer cells display significant changes in their cellular lipid homeostasis. These changes have been linked to the modulation of de novo lipid synthesis pathways that are markedly altered under lipid-deprived growth conditions. However, the effects of such environment on intracellular lipid trafficking-that could also affect cellular lipid homeostasis-have not been widely investigated. The presented work studies the effect of lipid deprivation on expression of genes for lipid transport proteins (LTPs) in cancer cell lines.