Amitriptyline down-regulates coenzyme Q10 biosynthesis in lung cancer cells

The role of coenzyme Q10 as a preventive and therapeutic agent for the treatment of cancers

Currently, several options are available for the prevention and treatment of cancers; however, many limitations remain with these approaches. Recently, antioxidants have become important preventive and therapeutic alternatives with few adverse events and minimum cost. Coenzyme Q10 (CoQ10) is a naturally occurring component that performs an anticancer function by reducing oxidative stress. CoQ10 supplementation as an adjuvant therapy offers more progress in the elimination and development of cancers. This review aimed to critically assess and summarize the implication of CoQ10 in cancers, highlighting possible mechanisms, and future directions of research for the standardization of the current regimen for cancer prevention and treatment.

Biosynthesis, Deficiency, and Supplementation of Coenzyme Q

Originally identified as a key component of the mitochondrial respiratory chain, Coenzyme Q (CoQ or CoQ₁₀ for human tissues) has recently been revealed to be essential for many different redox processes, not only in the mitochondria, but elsewhere within other cellular membrane types. Cells rely on endogenous CoQ biosynthesis, and defects in this still-not-completely understood pathway result in primary CoQ deficiencies, a group of conditions biochemically characterised by decreased tissue CoQ levels, which in turn are linked to functional defects. Secondary CoQ deficiencies may result from a wide variety of cellular dysfunctions not directly linked to primary synthesis. In this article, we review the current knowledge on CoQ biosynthesis, the defects leading to diminished CoQ₁₀ levels in human tissues and their associated clinical manifestations.

The application of antidepressant drugs in cancer treatment

Antidepressants refer to psychotropic drugs which are used to treat mental illness with prominent emotional depression symptoms. It was reported that antidepressants had associated with anti-carcinogenic function which was associated with various signaling pathways and changing of microenvironment. Its mechanism includes cell apoptosis, antiproliferative effects, mitochondria-mediated oxidative stress, DNA damaging, changing of immune response and inflammatory conditions, and acting by inhibiting multidrug resistance of cancer cells. Accumulated studies showed that antidepressants influenced the metabolic pathway of tumor cells. This review summarized recent developments with the impacts and mechanisms of 10 kinds of antidepressants in carcinostasis. Antidepressants are also used in combination therapy with typical anti-tumor drugs which shows a synergic effect in anti-tumor. By contrast, the promotion roles of antidepressants in increasing cancer recurrence risk, mortality, and morbidity are also included. Further clinical experiments and mechanism analyses needed to be achieved. A full understanding of the underlying mechanisms of antidepressants-mediated anticarcinogenic effects may provide new clues for cancer prevention and clinical treatment.

Mitochondrial-Related Transcriptome Feature Correlates with Prognosis, Vascular Invasion, Tumor Microenvironment, and Treatment Response in Hepatocellular Carcinoma

Background

Hepatocellular carcinoma (HCC) is the most common subtype of primary liver cancer, which was highly correlated with metabolic dysfunction. Nevertheless, the association between nuclear mitochondrial-related transcriptome and HCC remained unclear.

Materials and Methods

A total of 147 nuclear mitochondrial-related genes (NMRGs) were downloaded from the MITOMAP: A Human Mitochondrial Genome Database. The training dataset was downloaded from The Cancer Genome Atlas (TCGA), while validation datasets were retrieved from the International Cancer Genome Consortium (ICGC) and Gene Expression Omnibus (GEO). The univariate and multivariate, and least absolute shrinkage and selection operator (LASSO) Cox regression analyses were applied to construct a NMRG signature, and the value of area under receiver operating characteristic curve (AUC) was utilized to assess the signature and nomogram. Then, data from the Genomics of Drug Sensitivity in Cancer (GDSC) were used for the evaluation of chemotherapy response in HCC.

Results

Functional enrichment of differentially expressed genes (DEGs) between HCC and paired normal tissue samples demonstrated that mitochondrial dysfunction was significantly associated with HCC development. Survival analysis showed a total of 35 NMRGs were significantly correlated with overall survival (OS) of HCC, and the LASSO Cox regression analysis further identified a 25-NMRG signature and corresponding prognosis score based on their transcriptional profiling. HCC patients were divided into high- and low-risk groups according to the median prognosis score, and high-risk patients had significantly worse OS (median OS: 27.50 vs. 83.18 months, P < 0.0001). The AUC values for OS at 1, 3, and 5 years were 0.79, 0.77, and 0.77, respectively. The prognostic capacity of NMRG signature was verified in the GSE14520 dataset and ICGC-HCC cohort. Besides, the NMRG signature outperformed each NMRG and clinical features in prognosis prediction and could also differentiate whether patients presented with vascular invasions (VIs) or not. Subsequently, a prognostic nomogram (C-index: 0.753, 95% CI: 0.703~0.804) by the integration of age, tumor metastasis, and NMRG prognosis score was constructed with the AUC values for OS at 1, 3, and 5 years were 0.82, 0.81, and 0.82, respectively. Notably, significant enrichment of regulatory and follicular helper T cells in high-risk group indicated the potential treatment of immune checkpoint inhibitors for these patients. Interestingly, the NMRG signature could also identify the potential responders of sorafenib or transcatheter arterial chemoembolization (TACE) treatment. Additionally, HCC patients in high-risk group appeared to be more sensitive to cisplatin, vorinostat, and methotrexate, reversely, patients in low-risk group had significantly higher sensitivity to paclitaxel and bleomycin instead.

Conclusions

In summary, the development of NMRG signature provided a more comprehensive understanding of mitochondrial dysfunction in HCC, helped predict prognosis and tumor microenvironment, and provided potential targeted therapies for HCC patients with different NMRG prognosis scores.

HTR1A Inhibits the Progression of Triple-Negative Breast Cancer via TGF-β Canonical and Noncanonical Pathways

Triple-negative breast cancer is the most aggressive subtype of breast cancer and the incidence of depression in breast cancer patients is high, which leading to worse survival and increased risk of recurrence. The effect of antidepressants on breast cancer patients remains contradictory, which might be due to variations in antidepression targets. Therefore, there is significant value to explore the antitumor potential of antidepressants and discover new therapeutic targets for breast patients. The authors screen antidepressant-related oncogenes or suppressors by using siRNAs. After combining functional experiments with online database analysis, 5-hydroxytryptamine receptor 1A (HTR1A is selected with antitumor potential in breast cancer cells in vivo and in vitro. RNA-seq analysis and coimmunoprecipitation assays indicate that HTR1A interacts with TRIM21 and PSMD7 to inhibit the degradation of TβRII through the ubiquitin-proteasome pathway, thereby inhibiting the transforming growth factor-β (TGF-β) canonical and noncanonical pathway. In addition, HTR1A is an independent predictive factor for breast cancer patients. The combined treatment of HTR1A agonists with demethylation drugs may significantly improve patient survival. It is of great significance to clarify the function and mechanism of the depression-related gene HTR1A in breast cancer, which might provide a new approach for triple-negative breast cancer patients.

Machine learning revealed stemness features and a novel stemness-based classification with appealing implications in discriminating the prognosis, immunotherapy and temozolomide responses of 906 glioblastoma patients

Glioblastoma (GBM) is the most malignant and lethal intracranial tumor, with extremely limited treatment options. Immunotherapy has been widely studied in GBM, but none can significantly prolong the overall survival (OS) of patients without selection. Considering that GBM cancer stem cells (CSCs) play a non-negligible role in tumorigenesis and chemoradiotherapy resistance, we proposed a novel stemness-based classification of GBM and screened out certain population more responsive to immunotherapy. The one-class logistic regression algorithm was used to calculate the stemness index (mRNAsi) of 518 GBM patients from The Cancer Genome Atlas (TCGA) database based on transcriptomics of GBM and pluripotent stem cells. Based on their stemness signature, GBM patients were divided into two subtypes via consensus clustering, and patients in Stemness Subtype I presented significantly better OS but poorer progression-free survival than Stemness Subtype II. Genomic variations revealed patients in Stemness Subtype I had higher somatic mutation loads and copy number alteration burdens. Additionally, two stemness subtypes had distinct tumor immune microenvironment patterns. Tumor Immune Dysfunction and Exclusion and subclass mapping analysis further demonstrated patients in Stemness Subtype I were more likely to respond to immunotherapy, especially anti-PD1 treatment. The pRRophetic algorithm also indicated patients in Stemness Subtype I were more resistant to temozolomide therapy. Finally, multiple machine learning algorithms were used to develop a 7-gene Stemness Subtype Predictor, which were further validated in two external independent GBM cohorts. This novel stemness-based classification could provide a promising prognostic predictor for GBM and may guide physicians in selecting potential responders for preferential use of immunotherapy.