Complex Effects of Sertraline and Citalopram on In Vitro Murine Breast Cancer Proliferation and on In Vivo Progression and Anxiety Level

Antidepressant Sertraline Hydrochloride Inhibits the Growth of HER2+ AU565 Breast Cancer Cell Line through Induction of Apoptosis and Cell Cycle Arrest

BACKGROUND

Drug repurposing in oncology promises benefits to many patients through its ability to provide novel, and fast-tracked treatments. Previous studies have demonstrated that depression may influence tumor progression. Anti-proliferative activity of certain antidepressants, mainly selective serotonin reuptake inhibitors (SSRIs), are reported in the literature.

OBJECTIVE

This study was conducted to repurpose selective serotonin reuptake inhibitors (SSRIs) for the treatment of breast cancers, and it merits further validation and research.

METHODS

Changes in cell morphology were studied using DAPI staining, while the Annexin V/PI method was employed for apoptotic analysis. The expression of specific genes involved in cancer progression was also analyzed via RT-PCR. Caspase-3 activation was measured through fluorometric assay.

RESULTS

We have identified that sertraline hydrochloride significantly inhibited the growth of breast cancer cell in vitro. Preliminary mechanistic studies demonstrated that the cytotoxicity of sertraline hydrochloride was possibly through the induction of apoptosis, as inferred from enhanced nuclear fragmentation, flow cytometric data, and caspase-3/7 activation. Gene expression analysis also showed an increased expression of pro-apoptotic Bax, and a slight decrease in oncogene c-myc in the presence of sertraline hydrochloride.

CONCLUSION

In conclusion, our study suggest that sertraline hydrochloride, an antidepressant drug, can potentially be used for the treatment of breast cancer.

System analysis based on the cuproptosis-related genes identifies LIPT1 as a novel therapy target for liver hepatocellular carcinoma

BACKGROUND

Liver hepatocellular carcinoma (LIHC) ranks sixth among the most common types of cancer with a high mortality rate. Cuproptosis is a newly discovered type of cell death in tumor, which is characterized by accumulation of intracellular copper leading to the aggregation of mitochondrial lipoproteins and destabilization of proteins. Thus, understanding the exact effects of cuproptosis-related genes in LIHC and determining their prognosticvalue is critical. However, the prognostic model of LIHC based on cuproptosis-related genes has not been reported.

METHODS

Firstly, we downloaded transcriptome data and clinical information of LIHC patients from TCGA and GEO (GSE76427), respectively. We then extracted the expression of cuproptosis-related genes and established a prognostic model by lasso cox regression analysis. Afterwards, the prediction performance of the model was evaluated by Kaplan-Meier survival analysis and receiver operating characteristic curve (ROC). Then, the prognostic model and the expression levels of the three genes were validated using the dataset from GEO. Subsequently, we divided LIHC patients into two subtypes by non-negative matrix factorization (NMF) classification and performed survival analysis. We constructed a Sankey plot linking different subtypes and prognostic models. Next, we calculate the drug sensitivity of each sample from patients in the high-risk group and low-risk group by the R package pRRophetic. Finally, we verified the function of LIPT1 in LIHC.

RESULTS

Using lasso cox regression analysis, we developed a prognostic risk model based on three cuproptosis-related genes (GCSH, LIPT1 and CDKN2A). Both in the training and in the test sets, the overall survival (OS) of LIHC patients in the low-risk group was significantly longer than that in the high-risk group. By performing NMF cluster, we identified two molecular subtypes of LIHC (C1 and C2), with C1 subtype having significantly longer OS and PFS than C2 subtype. The ROC analysis indicated that our model had a precisely predictive capacity for patients with LIHC. The multivariate Cox regression analysis indicated that the risk score is an independent predictor. Subsequently, we identified 71 compounds with IC50 values that differed between the high-risk and low-risk groups. Finally, we determined that knockdown of LIPT1 gene expression inhibited proliferation and invasion of hepatoma cells.

CONCLUSION

In this study, we developed a novel prognostic model for hepatocellular carcinoma based on cuproptosis-related genes that can effectively predict the prognosis of LIHC patients. The model may be helpful for clinicians to make clinical decisions for patients with LIHC and provide valuable insights for individualized treatment. Two distinct subtypes of LIHC were identified based on cuproptosis-related genes, with different prognosis and immune characteristics. In addition, we verified that LIPT1 may promote proliferation, invasion and migration of LIHC cells. LIPT1 might be a new potential target for therapy of LIHC.