The mechanism of cancer-depression comorbidity.

Cancer and depression are closely interrelated, particularly in patients with advanced cancer, who often present with comorbid anxiety and depression for various reasons. Recently, there has been a growing interest in the study of depression in cancer patients, with the aim of assessing the possible triggers, predictors, adverse events, and possible treatment options for depression in several common cancers. The objective of this narrative review is to synthesize the extant literature on the relationship between the occurrence and progression of depression in several common patient categories. The authors conducted a comprehensive review of 75 articles published in PubMed over the past five years. This review was further evaluated in the present paper. Ultimately, it was determined that depression is a prevalent and detrimental phenomenon among cancer patients, particularly those with advanced disease. Consequently, there is a pressing need to prioritize research and interventions aimed at improving the quality of life and psychosocial well-being of cancer patients, including those with advanced disease. The relationship between cancer and depression has been evolving dynamically in recent times. The current research findings indicate a strong association between cancer and depression. However, the direction of causality remains unclear. Focusing on depression in cancer patients may, therefore, be beneficial for these patients.

Sodium citrate targeting Ca2+/CAMKK2 pathway exhibits anti-tumor activity through inducing apoptosis and ferroptosis in ovarian cancer.

INTRODUCTION: Ovarian cancer (OC) is known for its high mortality rate. Although sodium citrate has anti-tumor effects in various cancers, its effect and mechanism in OC remain unclear. OBJECTIVES: To analyze the inhibitory effect of sodium citrate on ovarian cancer cells and the underlying mechanism. METHODS: Cell apoptosis was examined by TUNEL staining, flow cytometry, and ferroptosis was examined intracellular Fe2+, MDA, LPO assays, respectively. Cell metabolism was examined by OCR and ECAR measurements. Immunoblotting and immunoprecipitation were used to elucidate the mechanism. RESULTS: This study suggested that sodium citrate not only promoted ovarian cancer cell apoptosis but also triggeredferroptosis, manifested as elevated levels of Fe2+, LPO, MDA andlipid ROS production. On one hand, sodium citrate treatment led to a decrease of Ca2+ content in the cytosol by chelatingCa2+, which further inhibited the Ca2+/CAMKK2/AKT/mTOR signaling, thereby suppressing HIF1α-dependent glycolysis pathway and inducing cell apoptosis. On the other hand, the chelation of Ca2+ by sodium citrate resulted in inactivation of CAMKK2 and AMPK, leading to increase of NCOA4-mediated ferritinophagy, causing increased intracellular Fe2+ levels. More importantly, the inhibition of Ca2+/CAMKK2/AMPK signaling pathway reduced the activity of the MCU and Ca2+ concentration within the mitochondria, resulting in an increase in mitochondrial ROS. Additionally, metabolomic analysis indicated that sodium citrate treatment significantly increased de novo lipid synthesis. Altogether, these factors contributed to ferroptosis. As expected, Ca2+ supplementation successfully reversed the cell death and decreased tumor growth induced by sodium citrate. Inspiringly, it was found that coadministration of sodium citrate increased the sensitivity of OC cells to chemo-drugs. CONCLUSION: These results revealed that the sodium citrate exerted its anti-cancer activity by inhibiting Ca2+/CAMKK2-dependent cell apoptosis and ferroptosis. Sodium citrate will hopefully serve as a prospective compound for OC treatment and for improvingthe efficacy of chemo-drugs.

The opposite role of lactate dehydrogenase a (LDHA) in cervical cancer under energy stress conditions.

Due to insufficient and defective vascularization, the tumor microenvironment is often nutrient-depleted. LDHA has been demonstrated to play a tumor-promoting role by facilitating the glycolytic process. However, whether and how LDHA regulates cell survival in the nutrient-deficient tumor microenvironment is still unclear. Here, we sought to investigate the role and mechanism of LDHA in regulating cell survival and proliferation under energy stress conditions. Our results showed that the aerobic glycolysis levels, cell survival and proliferation of cervical cancer cells decreased significantly after inhibition of LDHA under normal culture condition while LDHA deficiency greatly inhibited glucose starvation-induced ferroptosis and promoted cell proliferation and tumor formation under energy stress conditions. Mechanistic studies suggested that glucose metabolism shifted from aerobic glycolysis to mitochondrial OXPHOS under energy stress conditions and LDHA knockdown increased accumulation of pyruvate in the cytosol, which entered the mitochondria and upregulated the level of oxaloacetate by phosphoenolpyruvate carboxylase (PC). Importantly, the increase in oxaloacetate production after absence of LDHA remarkably activated AMP-activated protein kinase (AMPK), which increased mitochondrial biogenesis and mitophagy, promoted mitochondrial homeostasis, thereby decreasing ROS level. Moreover, repression of lipogenesis by activation of AMPK led to elevated levels of reduced nicotinamide adenine dinucleotide phosphate (NADPH), which effectively resisted ROS-induced cell ferroptosis and enhanced cell survival under energy stress conditions. These results suggested that LDHA played an opposing role in survival and proliferation of cervical cancer cells under energy stress conditions, and inhibition of LDHA may not be a suitable treatment strategy for cervical cancer.

Medical image segmentation based on self-supervised hybrid fusion network.

Automatic segmentation of medical images has been a hot research topic in the field of deep learning in recent years, and achieving accurate segmentation of medical images is conducive to breakthroughs in disease diagnosis, monitoring, and treatment. In medicine, MRI imaging technology is often used to image brain tumors, and further judgment of the tumor area needs to be combined with expert analysis. If the diagnosis can be carried out by computer-aided methods, the efficiency and accuracy will be effectively improved. Therefore, this paper completes the task of brain tumor segmentation by building a self-supervised deep learning network. Specifically, it designs a multi-modal encoder-decoder network based on the extension of the residual network. Aiming at the problem of multi-modal feature extraction, the network introduces a multi-modal hybrid fusion module to fully extract the unique features of each modality and reduce the complexity of the whole framework. In addition, to better learn multi-modal complementary features and improve the robustness of the model, a pretext task to complete the masked area is set, to realize the self-supervised learning of the network. Thus, it can effectively improve the encoder's ability to extract multi-modal features and enhance the noise immunity. Experimental results present that our method is superior to the compared methods on the tested datasets.

STS1 and STS2 Phosphatase Inhibitor Baicalein Enhances the Expansion of Hematopoietic and Progenitor Stem Cells and Alleviates 5-Fluorouracil-Induced Myelosuppression.

STS1 and STS2, as the protein phosphatases that dephosphorylate FLT3 and cKIT, negatively regulate the self-renewal and differentiation of hematopoietic stem and progenitor cells (HSPCs). To obtain the small molecule inhibitors of STS1/STS2 phosphatase activity used to expand HSPCs both in vitro and in vivo, we establish an in vitro phosphatase assay using the recombinant proteins of the STS1/STS2 histidine phosphatase (HP) domain, by which we screened out baicalein (BC) as one of the effective inhibitors targeting STS1 and STS2. Then, we further demonstrate the direct binding of BC with STS1/STS2 using molecular docking and capillary electrophoresis and verify that BC can restore the phosphorylation of FLT3 and cKIT from STS1/STS2 inhibition. In a short-term in vitro culture, BC promotes profound expansion and enhances the colony-forming capacity of both human and mouse HSPCs along with the elevation of phospho-FLT3 and phospho-cKIT levels. Likewise, in vivo administration with BC significantly increases the proportions of short-term hematopoietic stem cells (ST-HSCs), multipotent progenitors (MPPs) and especially long-term HSCs (LT-HSCs) in healthy mouse bone marrow and increases the numbers of colony-forming units (CFU) formed by HSPCs as well. More importantly, pre-administration of BC significantly enhances the survival of mice with lethal 5-fluorouracil (5-FU) injection due to the alleviation of 5-FU-induced myelosuppression, as evidenced by the recovery of bone marrow histologic injury, the increased proportions of LT-HSCs, ST-HSCs and MPPs, and enhanced colony-forming capacity. Collectively, our study not only suggests BC as one of the small molecule candidates to stimulate HSPC expansion both in vitro and in vivo when needed in either physiologic or pathologic conditions, but also supports STS1/STS2 as potential therapeutic drug targets for HSPC expansion and hematopoietic injury recovery.

MM-UNet: A multimodality brain tumor segmentation network in MRI images.

The global annual incidence of brain tumors is approximately seven out of 100,000, accounting for 2% of all tumors. The mortality rate ranks first among children under 12 and 10th among adults. Therefore, the localization and segmentation of brain tumor images constitute an active field of medical research. The traditional manual segmentation method is time-consuming, laborious, and subjective. In addition, the information provided by a single-image modality is often limited and cannot meet the needs of clinical application. Therefore, in this study, we developed a multimodality feature fusion network, MM-UNet, for brain tumor segmentation by adopting a multi-encoder and single-decoder structure. In the proposed network, each encoder independently extracts low-level features from the corresponding imaging modality, and the hybrid attention block strengthens the features. After fusion with the high-level semantic of the decoder path through skip connection, the decoder restores the pixel-level segmentation results. We evaluated the performance of the proposed model on the BraTS 2020 dataset. MM-UNet achieved the mean Dice score of 79.2% and mean Hausdorff distance of 8.466, which is a consistent performance improvement over the U-Net, Attention U-Net, and ResUNet baseline models and demonstrates the effectiveness of the proposed model.

Identification of Key Genes Related With Aspartic Acid Metabolism and Corresponding Protein Expression in Human Colon Cancer With Postoperative Prognosis and the Underlying Molecular Pathways Prediction.

Objective: Colon cancer is one of the most frequent and lethal neoplasias. Altered metabolic activity is a well-known hallmark for cancer. The present study is aiming to screen key genes associated with tumor metabolism and construct a prognostic signature of colon cancer patients. Methods: Glutamine- and UC- metabolism related genes were downloaded from GSEA MsigDB. Three key genes were screened by Cox regression analysis with data samples downloaded from TCGA and GSE29623 database. Consistent clustering based on the prognostic genes identified was employed to divide the colon cancer samples into two clusters with significant OS differences. The mRNA and protein expression of the key genes in colon tissues and matched adjacent noncancerous tissues of 16 patients were detected by IHC, qPCR, and Western blot to validate the constructed clustering model. GO, GSVA, and IPA were used to predict the relevant metabolic pathways. Results: According to the three key genes identified, i.e., ASNS, CEBPA, and CAD, the cohort can be divided into two clusters with prognosis differences. Clinical specimen results confirmed that the risk model established was effective, and the different expression pattern of ASNS and CEBPA was correlated with TNM stage and lymph node metastasis, whilst that of CAD was correlated with post-operative tumor metastasis and recurrence. Molecular mechanism prediction indicated that CREB, insulin, and RNA Pol II were the key nodes affecting CEBPA and ASNS expression. Moreover, TIDE algorithm reflected the better immune response of the cluster with shorter OS. Further immune infiltration and checkpoints analyses provided important reference for clinicians to perform individualized immunotherapy. Conclusion: Differential expression profile of three aspartic acid metabolic-associated genes, ASNS, CEBPA, and CAD, can be considered as a risk model with a good evaluation effect on the prognosis of colon cancer patients.