scholarly journals Activable Multi-Modal Nanoprobes for Imaging Diagnosis and Therapy of Tumors

2021 ◽  
Vol 8 ◽  
Author(s):  
Yan Yang ◽  
Saisai Yue ◽  
Yuanyuan Qiao ◽  
Peisen Zhang ◽  
Ni Jiang ◽  
...  
Keyword(s):  
Tumor Microenvironment ◽  
Cancer Progression ◽  
Tumor Imaging ◽  
Malignant Tumors ◽  
Prognostic Indicators ◽  
Imaging Diagnosis ◽  
Diagnosis And Therapy ◽  
Reducing Conditions ◽  
Acidic Extracellular Ph ◽  
Accurate Treatment

Malignant tumors have become one of the major causes of human death, but there remains a lack of effective methods for tiny tumor diagnosis, metastasis warning, clinical efficacy prediction, and effective treatment. In this context, localizing tiny tumors via imaging and non-invasively extracting molecular information related to tumor proliferation, invasion, metastasis, and drug resistance from the tumor microenvironment have become the most fundamental tasks faced by cancer researchers. Tumor-associated microenvironmental physiological parameters, such as hypoxia, acidic extracellular pH, protease, reducing conditions, and so forth, have much to do with prognostic indicators for cancer progression, and impact therapeutic administrations. By combining with various novel nanoparticle-based activatable probes, molecular imaging technologies can provide a feasible approach to visualize tumor-associated microenvironment parameters noninvasively and realize accurate treatment of tumors. This review focuses on the recent achievements in the design of “smart” nanomedicine responding to the tumor microenvironment-related features and highlights state-of- the-art technology in tumor imaging diagnosis and therapy.

scholarly journals Enhanced Expression of CNTD2/CCNP Predicts Poor Prognosis in Bladder Cancer Based on the GSE13507

2021 ◽  
Vol 12 ◽  
Author(s):  
Mancheng Gong ◽  
Erlin Song ◽  
Guiying Huang ◽  
Wenjun Ni ◽  
Wenjing Dong ◽  
...  
Keyword(s):  
Bladder Cancer ◽  
Cancer Patients ◽  
Cancer Progression ◽  
Poor Prognosis ◽  
Malignant Tumors ◽  
High Expression ◽  
Prognostic Indicators ◽  
Bladder Tissue ◽  
Normal Bladder ◽  
Tcga Dataset

Bladder cancer is one of the most common urogenital malignancies in the world, and there are no adequate prognostic indicators. CNTD2 is one of the atypical cyclins, which may be related to the cell cycle and even the development of cancers. Early studies have shown that CNTD2 is closely related to the occurrence and development of many malignant tumors. However, the mechanism of CNTD2 in bladder cancer has not been reported. In our research, we explored the different expressions of CNTD2 between 411 bladder cancers and 19 normal bladder tissues based on the TCGA dataset. CNTD2-related signaling pathways were identified through the GSEA. We analyzed the associations of CNTD2 expression and bladder cancer progression and survival using GSE13507. Compared with 19 cases of normal bladder tissue, CNTD2 gene expression was increased in 411 cases of bladder cancer. The high expression of CNTD2 strongly correlated with grade (P < 0.0001), T classification (P = 0.0001), N classification (P = 0.00011), M classification (P = 0.044), age (P = 0.027), and gender (P = 0.0012). Bladder cancer patients with high CNTD2 expression had shorter overall survival (P < 0.001). In the meantime, univariate and multivariate analyses showed that the increased expression of CNTD2 was an independent factor for poor prognosis in bladder cancer patients (P < 0.001 and P < 0.001, respectively). CNTD2 expression is closely related to bladder cancer progression, and the high expression of CNTD2 may be an adverse biomarker in bladder cancer patients.

scholarly journals Regulation of prostate cancer progression by the tumor microenvironment

2016 ◽  
Vol 380 (1) ◽  
pp. 340-348 ◽  
Author(s):  
Stephen L. Shiao ◽  
Gina Chia-Yi Chu ◽  
Leland W.K. Chung
Keyword(s):  
Prostate Cancer ◽  
Tumor Microenvironment ◽  
Cancer Progression ◽  
Prostate Cancer Progression

scholarly journals Identification of Gastric Cancer-Related Circular RNA through Microarray Analysis and Bioinformatics Analysis

2018 ◽  
Vol 2018 ◽  
pp. 1-9 ◽  
Author(s):  
Wei Gu ◽  
Ying Sun ◽  
Xiong Zheng ◽  
Jin Ma ◽  
Xiao-Ying Hu ◽  
...  
Keyword(s):  
Gene Expression ◽  
Gastric Cancer ◽  
Microarray Analysis ◽  
Cancer Progression ◽  
Pathway Analysis ◽  
Bioinformatics Analysis ◽  
Malignant Tumors ◽  
Expression Profiles ◽  
Circular Rnas ◽  
Target Mrnas

Gastric cancer is one of the common malignant tumors worldwide. Increasing studies have indicated that circular RNAs (circRNAs) play critical roles in the cancer progression and have shown great potential as useful markers and therapeutic targets. However, the precise mechanism and functions of most circRNAs are still unknown in gastric cancer. In the present study, we performed a microarray analysis to detect circRNA expression changes between tumor samples and adjacent nontumor samples. The miRNA expression profiles were obtained from the National Center of Biotechnology Information Gene Expression Omnibus (GEO). The differentially expressed circRNAs and miRNAs were identified through fold change filtering. The interactions between circRNAs and miRNAs were predicted by Arraystar’s home-made miRNA target prediction software. After circRNA-related miRNAs and dysregulated miRNAs were intersected, 23 miRNAs were selected. The target mRNAs of miRNAs were predicted by TarBase v7.0. Gene ontology (GO) enrichment analysis and pathway analysis were performed using standard enrichment computational methods for the target mRNAs. The results of pathway analysis showed that p53 signaling pathway and hippo signal pathway were significantly enriched and CCND2 was a cross-talk gene associated with them. Finally, a circRNA-miRNA-mRNA regulation network was constructed based on the gene expression profiles and bioinformatics analysis results to identify hub genes and hsa_circRNA_101504 played a central role in the network.

scholarly journals Diet, Obesity, and Cancer Progression: Are Adipocytes the Link?

2013 ◽  
Vol 6 ◽  
pp. LPI.S10871 ◽  
Author(s):  
Paul Toren ◽  
Benjamin C. Mora ◽  
Vasundara Venkateswaran
Keyword(s):  
Tumor Microenvironment ◽  
Cancer Progression ◽  
Tumor Biology ◽  
Model Systems ◽  
In Vivo Model ◽  
Diet Induced Obesity ◽  
Breast And Prostate Cancer ◽  
Obesity And Cancer

Obesity has been linked to more aggressive characteristics of several cancers, including breast and prostate cancer. Adipose tissue appears to contribute to paracrine interactions in the tumor microenvironment. In particular, cancer-associated adipocytes interact reciprocally with cancer cells and influence cancer progression. Adipokines secreted from adipocytes likely form a key component of the paracrine signaling in the tumor microenvironment. In vitro coculture models allow for the assessment of specific adipokines in this interaction. Furthermore, micronutrients and macronutrients present in the diet may alter the secretion of adipokines from adipocytes. The effect of dietary fat and specific fatty acids on cancer progression in several in vivo model systems and cancer types is reviewed. The more common approaches of caloric restriction or diet-induced obesity in animal models establish that such dietary changes modulate tumor biology. This review seeks to explore available evidence regarding how diet may modulate tumor characteristics through changes in the role of adipocytes in the tumor microenvironment.

scholarly journals Application of Deep Learning in Lung Cancer Imaging Diagnosis

2022 ◽  
Vol 2022 ◽  
pp. 1-12
Author(s):  
Wenfa Jiang ◽  
Ganhua Zeng ◽  
Shuo Wang ◽  
Xiaofeng Wu ◽  
Chenyang Xu
Keyword(s):  
Lung Cancer ◽  
Computer Vision ◽  
Deep Learning ◽  
Malignant Tumors ◽  
Lung Nodule ◽  
Ct Images ◽  
Lung Nodules ◽  
X Rays ◽  
Imaging Diagnosis ◽  
Risk Of Cancer

Lung cancer is one of the malignant tumors with the highest fatality rate and nearest to our lives. It poses a great threat to human health and it mainly occurs in smokers. In our country, with the acceleration of industrialization, environmental pollution, and population aging, the cancer burden of lung cancer is increasing day by day. In the diagnosis of lung cancer, Computed Tomography (CT) images are a fairly common visualization tool. CT images visualize all tissues based on the absorption of X-rays. The diseased parts of the lung are collectively referred to as pulmonary nodules, the shape of nodules is different, and the risk of cancer will vary with the shape of nodules. Computer-aided diagnosis (CAD) is a very suitable method to solve this problem because the computer vision model can quickly scan every part of the CT image of the same quality for analysis and will not be affected by fatigue and emotion. The latest advances in deep learning enable computer vision models to help doctors diagnose various diseases, and in some cases, models have shown greater competitiveness than doctors. Based on the opportunity of technological development, the application of computer vision in medical imaging diagnosis of diseases has important research significance and value. In this paper, we have used a deep learning-based model on CT images of lung cancer and verified its effectiveness in the timely and accurate prediction of lungs disease. The proposed model has three parts: (i) detection of lung nodules, (ii) False Positive Reduction of the detected nodules to filter out “false nodules,” and (iii) classification of benign and malignant lung nodules. Furthermore, different network structures and loss functions were designed and realized at different stages. Additionally, to fine-tune the proposed deep learning-based mode and improve its accuracy in the detection Lung Nodule Detection, Noudule-Net, which is a detection network structure that combines U-Net and RPN, is proposed. Experimental observations have verified that the proposed scheme has exceptionally improved the expected accuracy and precision ratio of the underlined disease.

scholarly journals Ferroptosis-mediated Crosstalk in the Tumor Microenvironment Implicated in Cancer Progression and Therapy

2021 ◽  
Vol 9 ◽  
Author(s):  
Yini Liu ◽  
Chunyan Duan ◽  
Rongyang Dai ◽  
Yi Zeng
Keyword(s):  
Cell Death ◽  
Tumor Microenvironment ◽  
Cancer Progression ◽  
Cancer Biology ◽  
Lipid Peroxides ◽  
Antioxidant Systems ◽  
Biological Processes ◽  
The Novel ◽  
Regulated Cell Death ◽  
Novel Therapeutic Strategies

Ferroptosis is a recently recognized form of non-apoptotic regulated cell death and usually driven by iron-dependent lipid peroxidation and has arisen to play a significant role in cancer biology. Distinct from other types of cell death in morphology, genetics, and biochemistry, ferroptosis is characterized by the accumulation of lipid peroxides and lethal reactive oxygen species controlled by integrated oxidant and antioxidant systems. Increasing evidence indicates that a variety of biological processes, including amino acid, iron, lactate, and lipid metabolism, as well as glutathione, phospholipids, NADPH, and coenzyme Q10 biosynthesis, are closely related to ferroptosis sensitivity. Abnormal ferroptotic response may modulate cancer progression by reprogramming the tumor microenvironment (TME). The TME is widely associated with tumor occurrence because it is the carrier of tumor cells, which interacts with surrounding cells through the circulatory and the lymphatic system, thus influencing the development and progression of cancer. Furthermore, the metabolism processes play roles in maintaining the homeostasis and evolution of the TME. Here, this review focuses on the ferroptosis-mediated crosstalk in the TME, as well as discussing the novel therapeutic strategies for cancer treatment.

scholarly journals Increased STIP1 and Hsp90 Correlate with Progression and Prognosis of Lung Adenocarcinoma

2021 ◽  
Author(s):  
Biaoxue Rong ◽  
Youwen Zhang ◽  
Junye Wang ◽  
Shucheng Ye ◽  
Maoqing Guo ◽  
...  
Keyword(s):  
Lung Adenocarcinoma ◽  
Malignant Tumors ◽  
Clinical Stage ◽  
A549 Cells ◽  
Biological Behavior ◽  
Prognostic Indicators ◽  
Normal Lung ◽  
Clinicopathological Parameters ◽  
Strong Positive Correlation ◽  
Advanced Clinical Stage

Abstract Background: Stress-inducible phosphoprotein 1 (STIP1) and heat shock protein 90 (Hsp90) have been found to be correlated with malignant tumors. The aim of this investigation was to study the relationship between their expressions and lung adenocarcinoma (LAC). Methods: The expressions of STIP1and Hsp90 in LAC cells and tissues were tested by immunohistochemistry and western blot; the correlation between their expressions and clinicopathological parameters of LAC was analyzed by survival analysisand multiple regression analysis. Results: Expressions of STIP1 and Hsp90 were higher in A549 cells and LAC tissues than that in 16 human bronchial epithelial cells (16HBE cells) (P<0.05) and adjacent normal lung tissues (P < 0.05). The expression of STIP1 and Hsp90 in LAC showed a strong positive correlation (P < 0.05) and significantly correlated with lymph node metastasis (P < 0.05), advanced clinical stage (P < 0.05) and shorter survival (P < 0.05) of LAC. Conclusions: Increased expressions of STIP1 and Hsp90 were closely related to malignant biological behavior of LAC, suggesting that they could be used as potential biomarkers and prognostic indicators for LAC.

scholarly journals Hypoxia-inducible factor (HIF): fuel for cancer progression

2021 ◽  
Vol 14 ◽  
Author(s):  
Saurabh Satija ◽  
Harpreet Kaur ◽  
Murtaza M. Tambuwala ◽  
Prabal Sharma ◽  
Manish Vyas ◽  
...  
Keyword(s):  
Biological Sciences ◽  
Tumor Microenvironment ◽  
Tumor Cells ◽  
Cancer Progression ◽  
Oxygen Deficiency ◽  
Hypoxia Inducible Factor ◽  
Transcription Factor Activation ◽  
Underlying Mechanisms ◽  
Adaptive Reactions

Hypoxia is an integral part of tumor microenvironment, caused primarily due to rapidly multiplying tumor cells and a lack of proper blood supply. Among the major hypoxic pathways, HIF-1 transcription factor activation is one of the widely investigated pathways in the hypoxic tumor microenvironment (TME). HIF-1 is known to activate several adaptive reactions in response to oxygen deficiency in tumor cells. HIF-1 has two subunits, HIF-1β (constitutive) and HIF-1α (inducible). The HIF-1α expression is largely regulated via various cytokines (through PI3K-ACT-mTOR signals), which involves the cascading of several growth factors and oncogenic cascades. These events lead to the loss of cellular tumor suppressant activity through changes in the level of oxygen via oxygen-dependent and oxygen-independent pathways. The significant and crucial role of HIF in cancer progression and its underlying mechanisms have gained much attention lately among the translational researchers in the fields of cancer and biological sciences, which have enabled them to correlate these mchanisms with various other disease modalities. In the present review, we have summarized the key findings related to the role of HIF in the progression of tumors.

scholarly journals Lung tumors: Early and delayed ratio of 99mTc-methoxy-2-isobutylisonitrile accumulation

2014 ◽  
Vol 71 (5) ◽  
pp. 438-445 ◽  
Author(s):  
Katarina Nikoletic ◽  
Jasna Mihailovic ◽  
Dolores Srbovan ◽  
Violeta Kolarov ◽  
Radmila Zeravica
Keyword(s):  
Tumor Imaging ◽  
Lung Tumor ◽  
Single Photon ◽  
Malignant Tumors ◽  
Photon Emission ◽  
Lung Tumors ◽  
Benign Tumors ◽  
Emission Computed Tomography ◽  
Uptake Ratio ◽  
Two Phase

Background/Aim. Currently used radiopharmaceuticals are nonspecific and most of them are accumulated by benign tumors as well as inflammatory lesions, abscess or granulomatous lesions. Some factors such as the choice of radiopharmaceutical applied, histopathologic type of tumor, its size, location or previous tumor treatment could influence tumor imaging sensitivity. The aim of this study was to investigate accumulation of 99mTc-methoxy-2-isobutylisonitrile (99mTc- MIBI) by counting early/delayed uptake and release of this radiopharmaceutical inside lung tumors and evaluating possible factors which could be involved in its accumulation. Methods. Two-phase 99mTc-methoxy-2-isobutylisonitrile single photon emission computed tomography scan (early and delayed scan) was performed in 60 patients with lung tumors (the group 1 - 30 benign, and the group 2 - 30 malignant tumors). We calculated the uptake ratio on early (early ratio - ER), delayed images (delayed ratio - DR) and retention index (RI). Individual influence of etiology, diameter, localization, and histological type on uptake/release values was evaluated with regression analysis. Results. The values of ER and DR were significantly different in both groups (p < 0.01), showing lower values in benign vs malignant lung tumors (ER 1.36 ? 0.094 and DR 1.25 ? 0.089 vs ER = 1.93 ? 0.106 and DR = 1.7 ? 0.095 respectively). Tumor size showed a significant influence on the change of ER and DR values (p < 0.01), with greater uptake in tumors > 3 cm. RI values showed no significance between the two groups (p > 0.05). Conclusion. The uptake ratio of 99mTc-methoxy-2-isobutylisonitrile could be a useful index in differentiating lung tumors, while RI has no influence on this. Among the evaluated factors, ER and DR values are significantly influenced only by the diameter of lung tumor, while localization or different histological types between the groups has no influence on this.

scholarly journals An Introduction to the Mathematical Modeling in the Study of Cancer Systems Biology

2018 ◽  
Vol 17 ◽  
pp. 117693511879975 ◽  
Author(s):  
Abdallah K Alameddine ◽  
Frederick Conlin ◽  
Brian Binnall
Keyword(s):  
Mathematical Modeling ◽  
Systems Biology ◽  
Cancer Progression ◽  
Malignant Tumors ◽  
Deterministic System ◽  
Introductory Overview ◽  
Cancer Systems Biology ◽  
Heterogeneous Processes ◽  
Global Systems ◽  
Deficient Mismatch Repair

Background: Frequently occurring in cancer are the aberrant alterations of regulatory onco-metabolites, various oncogenes/epigenetic stochasticity, and suppressor genes, as well as the deficient mismatch repair mechanism, chronic inflammation, or those deviations belonging to the other cancer characteristics. How these aberrations that evolve overtime determine the global phenotype of malignant tumors remains to be completely understood. Dynamic analysis may have potential to reveal the mechanism of carcinogenesis and can offer new therapeutic intervention. Aims: We introduce simplified mathematical tools to model serial quantitative data of cancer biomarkers. We also highlight an introductory overview of mathematical tools and models as they apply from the viewpoint of known cancer features. Methods: Mathematical modeling of potentially actionable genomic products and how they proceed overtime during tumorigenesis are explored. This report is intended to be instinctive without being overly technical. Results: To date, many mathematical models of the common features of cancer have been developed. However, the dynamic of integrated heterogeneous processes and their cross talks related to carcinogenesis remains to be resolved. Conclusions: In cancer research, outlining mathematical modeling of experimentally obtained data snapshots of molecular species may provide insights into a better understanding of the multiple biochemical circuits. Recent discoveries have provided support for the existence of complex cancer progression in dynamics that span from a simple 1-dimensional deterministic system to a stochastic (ie, probabilistic) or to an oscillatory and multistable networks. Further research in mathematical modeling of cancer progression, based on the evolving molecular kinetics (time series), could inform a specific and a predictive behavior about the global systems biology of vulnerable tumor cells in their earlier stages of oncogenesis. On this footing, new preventive measures and anticancer therapy could then be constructed.

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