The Role and Effect of Music Therapy in Combination Therapy for Cancer Management.

Cancer is one of the major diseases that harm human health. Tumor patients face a series of problems, including tumor disease symptoms, side effects of treatment, potential complications of illness, financial problems, and even the threat of death. The majority of patients are aware of these problems. It often manifests as fear, anxiety, anger, complaining, and depression. This leads to the generation of negative emotions, such as insanity and loneliness. These complications, such as feelings and isolation, seriously affect the quality of life of patients. Conventional treatments such as chemotherapy and radiotherapy cause some side effects and adverse reactions or difficulty in controlling some symptoms generated by the tumor itself. Therefore, some complementary and alternative medicines based on conventional cancer treatment have been added. In recent years, music nursing, as a non-traumatic and psychological therapy, has been applied to cancer patients, which plays a good role in improving their negative emotions and improving their quality of life. Moreover, this review summarizes the evolution of music therapy (MT), exploring the mechanism of MT and its clinical application in cancer management. In addition, some challenges and perspectives e.g., artificial intelligence applications for music therapy, are discussed. This work aims to encourage the broader application of MT for cancer patients to improve the survival experience of cancer patients, which is low cost, easy to implement, low risk, and efficient method.

Mitochondria-Targeted Nitronyl Nitroxide Radical Nanoparticles for Protection against Radiation-Induced Damage with Antioxidant Effects.

Radiotherapy is a non-invasive method that is widely applied to treat and alleviate cancers. However, radiation-induced effects in the immune system are associated with several side effects via an increase in oxidative stress and the inflammatory response. Therefore, it is imperative to develop effective clinical radiological protection strategies for the radiological protection of the normal organs and immune system in these patients. To explore more effective radioprotective agents with minimal toxicity, a mitochondria-targeted nitronyl nitroxide radical with a triphenylphosphine ion (TPP-NIT) was synthesized and its nanoparticles (NPs-TPP-NIT) were prepared and characterized. The TPP-NIT nanoparticles (NPs-TPP-NIT) were narrow in their size distribution and uniformly distributed; they showed good drug encapsulation efficiency and a low hemolysis rate (<3%). The protective effect of NPs-TPP-NIT against X-ray irradiation-induced oxidative damage was measured in vitro and in vivo. The results show that NPs-TPP-NIT were associated with no obvious cytotoxicity to L-02 cells when the concentration was below 1.5 × 10-2 mmol. NPs-TPP-NIT enhanced the survival rate of L-02 cells significantly under 2, 4, 6, and 8 Gy X-ray radiation exposure; the survival rate of mice was highest after 6 Gy X-ray irradiation. The results also show that NPs-TPP-NIT could increase superoxide dismutase (SOD) activity and decrease malondialdehyde (MDA) levels after the L-02 cells were exposed to 6.0 Gy of X-ray radiation. Moreover, NPs-TPP-NIT could significantly inhibit cell apoptosis. NPs-TPP-NIT significantly increased the mouse survival rate after irradiation. NPs-TPP-NIT displayed a marked ability to reduce the irradiation-induced depletion of red blood cells (RBCs), white blood cells (WBCs), and platelets (PLTs). These results demonstrate the feasibility of using NPs-TPP-NIT to provide protection from radiation-induced damage. In conclusion, this study revealed that NPs-TPP-NIT may be promising radioprotectors and could therefore be applied to protect healthy tissues and organs from radiation during the treatment of cancer with radiotherapy.

Intercellular Interactions Mediated by HGF And TGF-Β Promote the 3D Spherical and Xenograft Growth of Liver Cancer Cells.

BACKGROUND: Recently, the importance of the interactions between liver cancer cells and fibroblasts has been increasingly recognized; however, many details remain to be explored. METHODS: In this work, we first studied their intercellular interactions using conditioned medium from mouse embryonic fibroblasts (MEFs), then through a previously established coculture model. RESULTS: Culturing in a conditioned medium from MEFs could significantly increase the growth, migration, and invasion of liver cancer cells. The coculture model further demonstrated that a positive feedback loop was formed between transforming growth factor-ß (TGF-ß) from HepG2 cells and mHGF (mouse hepatocyte growth factor) from MEFs during coculture. In this feedback loop, c-Met expression in HepG2 cells was significantly increased, and its downstream signaling pathways, such as Src/FAK, PI3K/AKT, and RAF/MEK/ERK, were activated. Moreover, the proportion of activated MEFs was also increased. More importantly, the growth-promoting effects caused by the interaction of these two cell types were validated in vitro by a 3D spheroid growth assay and in vivo by a xenograft mouse model. CONCLUSION: Collectively, these findings provide valuable insights into the interactions between fibroblasts and liver cancer cells, which may have therapeutic implications for the treatment of liver cancer.

Navigating chimeric antigen receptor-engineered natural killer cells as drug carriers via three-dimensional mapping of the tumor microenvironment.

Chimeric antigen receptor (CAR)-modified natural killer (NK) cells are recognized as promising immunotherapeutic agents for cancer treatment. However, the efficacy and trafficking of CAR-NK cells in solid tumors are hindered by the complex barriers present in the tumor microenvironment (TME). We have developed a novel strategy that utilizes living CAR-NK cells as carriers to deliver anticancer drugs specifically to the tumor site. We also introduce a time-lapse method for evaluating the efficacy and tumor specificity of CAR-NK cells using a two-photon microscope in live mouse models and three-dimensional (3D) tissue slide cultures. Our results demonstrate that CAR-NK cells exhibit enhanced antitumor immunity when combined with photosensitive chemicals in both in vitro and in vivo tumor models. Additionally, we have successfully visualized the trafficking, infiltration, and accumulation of drug-loaded CAR-NK cells in deeply situated TME using non-invasive intravital two-photon microscopy. Our findings highlight that tumor infiltration of CAR-NK cells can be intravitally monitored through the two-photon microscope approach. In conclusion, our study demonstrates the successful integration of CAR-NK cells as drug carriers and paves the way for combined cellular and small-molecule therapies in cancer treatment. Furthermore, our 3D platform offers a valuable tool for assessing the behavior of CAR cells within solid tumors, facilitating the development and optimization of immunotherapeutic strategies with clinical imaging approaches.

Evaluation of treatments with radiotherapy alone and radiotherapy plus chemo-immunotherapy in patients with primary liver cancer based on blood biomarkers.

PURPOSE: It is critical to assess primary liver cancer patients likely to benefit from radiotherapy (RT) or RT plus chemo-immunotherapy. Many potential peripheral biomarkers from blood samples have been proposed for clinical application. Therefore, the aim of this study was to evaluate treatments with radiotherapy alone and radiotherapy plus chemo-immunotherapy in patients with unresectable primary liver cancer based on blood biomarkers. METHODS: From January, 2017, to February, 2022, 63 unresectable primary liver cancer patients receiving radiotherapy alone (RT, n = 21) or radiotherapy plus chemo-immunotherapy (RT plus C/IT, n = 42) were included in this study. We compared the clinical outcomes and adverse effects of these two groups. Also, distant metastasis-free survival (DMFS), overall survival (OS), and progress-free survival (PFS) were retrospectively analyzed. Finally, univariable and multivariable Cox analyses were used to explore the prognostic role of blood biochemical biomarkers. RESULTS: In this study, 1, 2, and 3 years of OS after RT treatment were 63.9%, 27.0%, and 13.5%, and after RT plus C/IT were 68.2%, 37.0%, and 24.7%, respectively (p = 0.617). Compared with baseline, white blood cells (WBC) and lymphocytes were significantly decreased after RT (p=0.002 and p=0.001, respectively) or RT plus C/IT therapy (p=0.135 and p<0.001, respectively). In multivariable Cox regression analyses, higher lymphocyte counts before RT (pre-Lymphocyte) were associated with better OS and PFS (HR=0.439, p=0.023; HR=0.539, p=0.053; respectively), and higher lymphocyte counts before RT (pre- Platelets) were a poor prognostic factor associated with DMFS (HR=1.013, p=0.040). Importantly, OS and PFS were significantly better for patients (pre-Lymphocyte ≥1.10 x 109/L) (p=0.006; p=0.066, respectively). The DMFS was significantly better for patients (pre-platelets < 233.5 ×109/L) (p<0.001). CONCLUSION: Our evaluation of blood biomarkers before and after radiotherapy or plus chem-immunotherapy for primary liver cancer revealed a potential marker for clinics to decide on precise treatment strategies.

The Effects of Ginkgo biloba Extract on Autophagy in Human Macrophages Stimulated by Cigarette Smoke Extract.

OBJECTIVE: To investigate the effects of Ginkgo biloba extract (GBE) on autophagy in human macrophages stimulated by cigarette smoke extract (CSE). METHODS: The human monocyte cell line U937 was cultured in vitro, and phorbol ester (PMA) was added to the cell culture medium to induce differentiation into human macrophages. CSE was prepared by traditional methods for experiments. The cells were divided into four groups: the blank group, the CSE model group, the GBE + CSE group, and the rapamycin + CSE group. Immunofluorescence was used to identify human macrophages, transmission electron microscopy was used to observe the ultrastructure of human macrophages in each group, ELISA was used to measure the amount of IL-6 and IL-10 in the supernatant from each group of cells, the mRNA levels of p62, ATG5, ATG7, and Rab7 were measured by real-time qPCR, and the protein expression levels of p62, ATG5, ATG7, and Rab7 were measured by Western blotting. RESULTS: U937 cells were successfully differentiated into human macrophages after induction with PMA. The CSE model group had many more autophagosomes than the blank group. Compared with the CSE model group, the GBE + CSE group and the rapamycin + CSE group had significantly more autophagolysosomal. Compared with the other groups, the CSE model group had a higher level of IL-6 but a lower level of IL-10 in the supernatant (p < 0.05). Compared with the blank group, the mRNA and protein expression levels of p62 in the CSE model group were significantly decreased, while the mRNA and protein expression levels of ATG5 and ATG7 were significantly increased in the CSE model group (p < 0.05). No difference was found in the mRNA and protein expression levels of Rab7 between the blank group and the CSE model group. Compared with the CSE model group, the IL-6 level in the GBE + CSE group and the rapamycin + CSE group cell culture supernatant decreased significantly, p62 mRNA and protein expression significantly decreased, while ATG5, ATG7, and Rab7 mRNA and protein expression levels were significantly increased (p < 0.05). Moreover, increased LC3-II/LC3-I ratio were also found in the GBE + CSE group and the rapamycin + CSE group compared with the CSE model group. CONCLUSIONS: GBE could promote the fusion of autophagosomes and lysosomes in human macrophages, enhance the autophagy function of human macrophages, and reduce the damaging effect of CSE on the autophagy function of macrophages.

Group theoretic particle swarm optimization for multi-level threshold lung cancer image segmentation.

Background: Image segmentation is an important step during the processing of medical images. For example, for the computer aid diagnostic systems for lung cancer image analysis, the segmented regions of tumors would help doctors in early diagnosis to determine timely and appropriate treatment possibilities and thereby improve the survival rate of the patients. However, general clinical routines of manual segmentation for large number of medical images are very difficult and time consuming, which is the challenge we aim to tackle using our proposed method. Methods: A novel image segmentation method with evolutionary learning technique named Group Theoretic Particle Swarm Optimization is proposed. It can tackle multi-level thresholding optimization problem during the segmentation process and rebuild the search paradigm according to the solid mathematical foundation of symmetric group from four designable aspects, which are particle encoding, solution landscape, neighborhood movement and swarm topology, respectively. The Kapur's entropy of multi-level thresholds is assessed as the objective function. Results: In contrast to those conventional metaheuristics methods for lung cancer image segmentation, this newly presented method generates the best performance result among them. Experimental results show that its Kapur's entropy has the value of 9.07, which is 16% higher than the worst case. Computational time is acceptable at the cost of 173.730 seconds, average level of evaluation metrics [Kappa, Precision, Recall, F1-measure, intersection over union (IoU) and receiver operating characteristic (ROC)] is over 90%, and search process of multi-level threshold combination would finally converge in the later phase of iterations after 700. The ablation study indicates that all components are significant to the contributions of our proposed method. Conclusions: Group Theoretic Particle Swarm Optimization for multi-level threshold segmentation is an efficient way to split a medical image into distinct regions and extract tumor tissues regions from the background. It maintains the balanced relationship between diversification and intensification during the search process and helps clinicians to make the diagnosis more accurately. Our proposed method processes potential medical value and clinical meanings.

Forecasting Tourist Arrivals for Hainan Island in China with Decomposed Broad Learning before the COVID-19 Pandemic.

This study proposes a decomposed broad learning model to improve the forecasting accuracy for tourism arrivals on Hainan Island in China. With decomposed broad learning, we predicted monthly tourist arrivals from 12 countries to Hainan Island. We compared the actual tourist arrivals to Hainan from the US with the predicted tourist arrivals using three models (FEWT-BL: fuzzy entropy empirical wavelet transform-based broad learning; BL: broad Learning; BPNN: back propagation neural network). The results indicated that US foreigners had the most arrivals in 12 countries, and FEWT-BL had the best performance in forecasting tourism arrivals. In conclusion, we establish a unique model for accurate tourism forecasting that can facilitate decision-making in tourism management, especially at turning points in time.

Artificial intelligence in lung cancer diagnosis and prognosis: Current application and future perspective.

Lung cancer is one of the malignant tumors with the highest incidence and mortality in the world. The overall five-year survival rate of lung cancer is relatively lower than many leading cancers. Early diagnosis and prognosis of lung cancer are essential to improve the patient's survival rate. With artificial intelligence (AI) approaches widely applied in lung cancer, early diagnosis and prediction have achieved excellent performance in recent years. This review summarizes various types of AI algorithm applications in lung cancer, including natural language processing (NLP), machine learning and deep learning, and reinforcement learning. In addition, we provides evidence regarding the application of AI in lung cancer diagnostic and clinical prognosis. This review aims to elucidate the value of AI in lung cancer diagnosis and prognosis as the novel screening decision-making for the precise treatment of lung cancer patients.

Efficacy and safety of PEG-rhG-CSF in preventing chemoradiotherapy-induced neutropenia in patients with locally advanced cervical cancer.

The standard of care for locally advanced cervical cancer is concurrent chemoradiotherapy, which is associated with significant toxicity, especially hematologic toxicity. To evaluate the efficacy and safety of pegylated recombinant human granulocyte colony-stimulating factor (PEG-rhG-CSF) in preventing neutropenia during radical chemoradiotherapy for cervical cancer, 40 patients receiving prophylaxis from February 2018 to July 2019 were randomly divided into two arms in a 1:1 ratio. Patients in the study arm (N = 21) received PEG-rhG-CSF, while patients in the control arm (N = 19) received short-acting rhG-CSF. The primary endpoint was the incidence of grade 3-4 neutropenia, and the secondary endpoints were the incidence of febrile neutropenia, chemotherapy delay, and radiotherapy interruption. In addition, dynamic changes in absolute neutrophil count during radical chemoradiotherapy and adverse events were compared between the two groups. There were 0 and 4 cycles of grade 3-4 neutropenia in the PEG-rhG-CSF and rhG-CSF groups, respectively. The incidence of neutropenia of all grades was lower in patients on PEG-rhG-CSF than on rhG-CSF [24.05% (19/79) vs. 56.94% (41/72); p < 0.001]. No patient developed neutropenic fever. The lowest values of neutropenia during concurrent chemoradiotherapy cycles were 2.73 ± 1.02 and 1.91 ± 0.79 × 10 9/ml in the PEG-rhG-CSF and rhG-CSF groups, respectively (p < 0.001). In the PEG-rhG-CSF and rhG-CSF groups, 0 and 8 (11.11%) cycles of chemotherapy were delayed due to neutropenia, respectively (p = 0.01). There was no delay of radiotherapy by more than one week in either group. Prophylactic use of PEG-rhG-CSF during chemoradiotherapy for cervical cancer can effectively prevent neutropenia and associated adverse events. PEG-rhG-CSF may be an effective strategy to provide uninterrupted radical chemoradiotherapy for cervical cancer.

Automated Prediction of Neoadjuvant Chemoradiotherapy Response in Locally Advanced Cervical Cancer Using Hybrid Model-Based MRI Radiomics.

BACKGROUND: This study aimed to develop a model that automatically predicts the neoadjuvant chemoradiotherapy (nCRT) response for patients with locally advanced cervical cancer (LACC) based on T2-weighted MR images and clinical parameters. METHODS: A total of 138 patients were enrolled, and T2-weighted MR images and clinical information of the patients before treatment were collected. Clinical information included age, stage, pathological type, squamous cell carcinoma (SCC) level, and lymph node status. A hybrid model extracted the domain-specific features from the computational radiomics system, the abstract features from the deep learning network, and the clinical parameters. Then, it employed an ensemble learning classifier weighted by logistic regression (LR) classifier, support vector machine (SVM) classifier, K-Nearest Neighbor (KNN) classifier, and Bayesian classifier to predict the pathologic complete response (pCR). The area under the receiver operating characteristics curve (AUC), accuracy (ACC), true positive rate (TPR), true negative rate (TNR), and precision were used as evaluation metrics. RESULTS: Among the 138 LACC patients, 74 were in the pCR group, and 64 were in the non-pCR group. There was no significant difference between the two cohorts in terms of tumor diameter (p = 0.787), lymph node (p = 0.068), and stage before radiotherapy (p = 0.846), respectively. The 109-dimension domain features and 1472-dimension abstract features from MRI images were used to form a hybrid model. The average AUC, ACC, TPR, TNR, and precision of the proposed hybrid model were about 0.80, 0.71, 0.75, 0.66, and 0.71, while the AUC values of using clinical parameters, domain-specific features, and abstract features alone were 0.61, 0.67 and 0.76, respectively. The AUC value of the model without an ensemble learning classifier was 0.76. CONCLUSIONS: The proposed hybrid model can predict the radiotherapy response of patients with LACC, which might help radiation oncologists create personalized treatment plans for patients.

Antitumor effects of polysaccharides from Tetrastigma hemsleyanum Diels et Gilg via regulation of intestinal flora and enhancing immunomodulatory effects in vivo.

Tetrastigma hemsleyanum Diels et Gilg is a traditional Chinese herbal medicine with high medicinal value, and antitumor, antioxidant and anti-inflammatory biological activities. However, while several studies have focused on flavonoids in Tetrastigma hemsleyanum tubers, there are few studies on the enhanced immune effect of Tetrastigma hemsleyanum polysaccharides (THP). In this study, we evaluated the antitumor effect of THP in a lung tumor model and explored the mechanism of antitumor activity through intestinal flora. In addition, a cyclophosphamide (CTX)-induced immunosuppression model was used to declare the immunomodulatory effect of THP in the immunosuppressive state induced by antitumor drugs. The results showed that THP increased the content of ileum secreted immunoglobulin A (SIgA) and cecum short-chain fatty acids (SCFAs) and improved microbial community diversity, regulating the relative abundance of dominant microbiota flora from the phylum level to the genus level, and recovering the intestinal microflora disorder caused by tumors. Additionally, THP can increase the organ indices and improve immune organ atrophy. THP can upregulate routine blood counts and stimulate the production of the serum cytokines. THP also promoted the macrophage phagocytic index, NK-cell activation, and complement and immunoglobulin (IgG, IgA, IgM) levels. The detection of Splenic lymphocyte proliferation and T lymphocyte subsets also sideways reflects that THP can restore CTX-induced immune inhibition in mice. In conclusion, this study suggests that THP can effectively achieve the enhanced antitumor effects, regulate gut microbiota and improve the immunosuppression induced by antitumor drugs. Therefore, THP can enhance the immune capacity and provide novel immunomodulatory and antineoplastic adjuvant agents.

Corrigendum: An in vivo fluorescence resonance energy transfer-based imaging platform for targeted drug discovery and cancer therapy.

[This corrects the article DOI: 10.3389/fbioe.2022.839078.].

Advances in the application of 3D tumor models in precision oncology and drug screening.

Traditional tumor models cannot perfectly simulate the real state of tumors in vivo, resulting in the termination of many clinical trials. 3D tumor models' technology provides new in vitro models that bridge the gap between in vitro and in vivo findings, and organoids maintain the properties of the original tissue over a long period of culture, which enables extensive research in this area. In addition, they can be used as a substitute for animal and in vitro models, and organoids can be established from patients' normal and malignant tissues, with unique advantages in clinical drug development and in guiding individualized therapies. 3D tumor models also provide a promising platform for high-throughput research, drug and toxicity testing, disease modeling, and regenerative medicine. This report summarizes the 3D tumor model, including evidence regarding the 3D tumor cell culture model, 3D tumor slice model, and organoid culture model. In addition, it provides evidence regarding the application of 3D tumor organoid models in precision oncology and drug screening. The aim of this report is to elucidate the value of 3D tumor models in cancer research and provide a preclinical reference for the precise treatment of cancer patients.

Sequential Hypofractionated versus Concurrent Twice-Daily Radiotherapy for Limited-Stage Small-Cell Lung Cancer: A Propensity Score-Matched Analysis.

Background: As there are no randomized trials comparing twice-daily with sequential hypofractionated (sequential hypo) radiotherapy regimens for limited-stage small-cell lung cancer (LS-SCLC). This study aimed to compare these two regimens for LS-SCLC by propensity score-matched analysis (PSM). Methods: We retrospectively analyzed 108 LS-SCLC patients between January 2015 and July 2019. All patients received concurrent twice-daily or sequential hypo radiotherapy. The survival, failure patterns, and toxicities were evaluated before and after PSM. Results: Before PSM, multivariate analysis showed that patients treated with sequential hypo had a significantly better overall survival (OS) and distant metastasis-free survival (DMFS) (HR = 0.353, p = 0.009; HR = 0.483, p = 0.039, respectively). Total radiotherapy time ≥ 24 days and stage III (HR = 2.454, p = 0.004; HR = 2.310, p = 0.004, respectively) were poor prognostic indicators for OS. Patients with a total radiotherapy time ≥ 24 days and N2−3 were more likely to recur than others (HR = 1.774, p = 0.048; HR = 2.369, p = 0.047, respectively). N2−3 (HR = 3.032, p = 0.011) was a poor prognostic indicator for DMFS. After PSM, being aged ≥65 years was associated with poorer OS, relapse-free survival (RFS) and DMFS (p < 0.05). A total radiotherapy time of ≥24 days was a poor prognostic indicator for OS and RFS (HR = 2.671, p = 0.046; HR = 2.370, p = 0.054, respectively). Although there was no significant difference, the patients in the sequential hypo group had a trend towards a better OS. The failure pattern between the two groups showed no difference. More patients had grade 1−2 esophagitis in the twice-daily group (p = 0.001). Conclusions: After propensity matching, no difference was shown in survival and failure. The sequential hypo schedule was associated with comparable survival and less toxicity and may be used as an alternative to concurrent twice-daily regimens.

Multi-Label Attribute Selection of Arrhythmia for Electrocardiogram Signals with Fusion Learning.

There are three primary challenges in the automatic diagnosis of arrhythmias by electrocardiogram (ECG): the significant variation among individual patients, the multiple pathologies in the ECG signal and the high cost in annotating clinical ECG with the corresponding labels. Traditional ECG processing approaches rely heavily on prior knowledge, such as those from feature extraction and waveform analysis. The preprocessing for prior knowledge incurs computational overhead. Furthermore, standard deep learning methods do not fully consider the dynamic temporal, spatial and multi-labeling characteristics of ECG data. In clinical ECG waveforms, it is common to see multi-labeling in which a patient is labeled with multiple classes of arrhythmias. However, multiclass approaches in current research mainly solve the multi-label machine learning problem, ignoring the correlation between diseases, resulting in information loss. In this paper, an arrhythmia detection and classification scheme called multi-label fusion deep learning is proposed. The objective is to build a unified system with automatic feature learning which supports effective multi-label classification. First, a multi-label ECG-based feature selection method is combined with a matrix decomposition and sparse learning theory. The optimal feature subset is selected as a preprocessing algorithm for ECG data. A multi-label classifier is then constructed by fusing CNN and RNN networks to fully exploit the interactions and features of the time and space dimensions. The experimental result demonstrates that the proposed method can achieve a state-of-the-art performance compared to other algorithms in multi-label database experiments.

Recent Progress on the Role of Fibronectin in Tumor Stromal Immunity and Immunotherapy.

As a major component of the stromal microenvironment of various solid tumors, the extracellular matrix (ECM) has attracted increasing attention in cancer-related studies. ECM in the tumor stroma not only provides an external barrier and framework for tumor cell adhesion and movement, but also acts as an active regulator that modulates the tumor microenvironment, including stromal immunity. Fibronectin (Fn), as a core component of the ECM, plays a key role in the assembly and remodeling of the ECM. Hence, understanding the role of Fn in the modulation of tumor stromal immunity is of great importance for cancer immunotherapy. Hence, in-depth studies on the underlying mechanisms of Fn in tumors are urgently needed to clarify the current understanding and issues and to identify new and specific targets for effective diagnosis and treatment purposes. In this review, we summarize the structure and role of Fn, its potent derivatives in tumor stromal immunity, and their biological effects and mechanisms in tumor development. In addition, we discuss the novel applications of Fn in tumor treatment. Therefore, this review can provide prospective insight into Fn immunotherapeutic applications in tumor treatment.

An In Vivo Fluorescence Resonance Energy Transfer-Based Imaging Platform for Targeted Drug Discovery and Cancer Therapy.

In the present study, an efficient in vivo drug screening platform is established based on FRET technique. We transfected cancer cells with FRET-based caspase-3 (C3) sensor and validated the cell lines by detecting the change in FRET signal caused by the in vitro drug-induced cell apoptosis. Furthermore, the C3 expressing cancer cells were then injected into zebrafish embryos and nude mice to establish the corresponding in vivo xenograft models. We found that cancer cell lines expressing C3 were effective in detecting cell death following drug treatment, including the detection of the tipping point of apoptosis. The drug-induced cell apoptosis was also observed in both zebrafish embryos and nude mice xenograft models. Overall, the FRET-based platform, through in vivo imaging, is potentially useful to improve drug screening efficiency.

Suppressor of cytokine signaling 6 in cancer development and therapy: Deciphering its emerging and suppressive roles.

The suppressor of cytokine signaling 6 (SOCS6), an emerged important member of SOCS family, has attracted enhancing attention regarding its pivotal role in the development and progression of cancers. As part of negative feedback regulation, SOCS6 has been implicated in attenuating cytokine signal transduction via inhibiting the signaling cascade of activated cytokine receptors and multiple receptor tyrosine kinase signaling. Decreased SOCS6 expression is involved in diverse tumorigenic processes, such as abnormal cell proliferation, evasion of apoptosis, cancer migration, and cancer stem cell maintenance. Herein, this review summarized the mechanisms of SOCS6 regulation underlying multiple pathways. In particular, we focus on the pathological processes of cancer targeted by SOCS6 and discuss its inhibitory role during tumor progression. Also, we focused on the clinical relevance of SOCS6 in cancer biomarker and prognosis, as well as its significance in chemoresistance and radioresistance. In all, this review pave a way to assist in experimental designs and emphasize the potential clinical value of SOCS6 for cancer.