Secreted Non-Coding RNAs: Functional Impact on the Tumor Microenvironment and Clinical Relevance in Triple-Negative Breast Cancer

Triple-negative breast cancer (TNBC) is a subtype of breast carcinoma characterized by poor prognosis and high rate of metastasis. Current treatment is based on chemo- and/or radiotherapy and surgery. TNBC is devoid of estrogen, progesterone and HER2 receptors. Although precision medicine has come a long way to ameliorate breast cancer disease management, targeted therapies for the treatment of TNBC patients are still limited. Mounting evidence has shown that non-coding RNAs (ncRNAs) drive many oncogenic processes at the basis of increased proliferation, invasion and angiogenesis in TNBC, strongly contributing to tumor progression and resistance to treatments. Many of these ncRNAs are secreted in the tumor microenvironment (TME) and impinge on the activity of the diverse immune and stromal cell types infiltrating the TME. Importantly, secreted ncRNAs may be detected as circulating molecules in serum/plasma from cancer patients and are emerging a promising diagnostic/therapeutic tools in TNBC. This review aims to discuss novel insights about the role of secreted circulating ncRNAs in the intercellular communication in the tumor microenvironment and their potential clinical use as diagnostic and prognostic non-invasive biomarkers in TNBC.

Impact of the COVID-19 pandemic on new starts to oral oncology medications in the US

Introduction The COVID-19 pandemic has had a significant impact on healthcare delivery. Although others have documented the impact on new cancer diagnoses, trends in new starts for oncology drugs are less clear. We examined changes in new users of oral oncology medications in the US following COVID-19 stay-at-home orders in 2020 compared to prior years. Methods We examined prescription data for members enrolled with a national pharmacy benefits manager in the US from January 1-October 31 of 2018, 2019, and/or 2020. This is a retrospective, observational study comparing new users per 100,000 members per month for all oral oncology drugs, and separately for breast, lung, and prostate cancer, leukemia, and melanoma oral drugs. We performed a difference-in-differences analysis for change in new users from pre-period (prior to pandemic-induced disruption, January-March), to post-period (following pandemic-induced disruption, April-October), between 2020 and 2019, and 2020 and 2018. Results New oral oncology drug users per 100,000 members per month declined by an additional 11.3% in the 2020 post-period compared to 2019 ( p = 0.048). New oral breast cancer drug starts declined by an additional 14.0% in the 2020 post-period compared to 2019 ( p = 0.040). Similar but non-significant trends were found between 2020 and 2018. No significant differences were found between post-period monthly new starts of leukemia, melanoma, lung or prostate cancer disease-specific oral medications. Conclusions Long-term implications of delays in cancer treatment initiation are unclear, although there is concern that patient outcomes may be negatively impacted.

A Robust Feature Extraction and Deep Learning Approach for Cancer Gene Prognosis

Mutated genes are one of the prominent factors in origination and spread of cancer disease. Here we have used Genomic signal processing methods to identify the patterns that differentiate cancer and non-cancerous genes. Furthermore, Deep learning algorithms were used to model a system that automatically predicts the cancer gene. Unlike the existing methods, two feature extraction modules are deployed to extract six attributes. Power Spectral Density based module was used to extract statistical parameters like Mean, Median, Standard deviation, Mean Deviation and Median Deviation. Adaptive Functional Link Network (AFLN) based filter module was used to extract Normalized Mean Square Error (NMSE). The uniqueness of this paper is identification of six input features that differentiates cancer genes. In this work artificial neural network is developed to predict cancer genes. Comparison is done on three sets of datasets with 6 attributes, 5 attributes and one attribute. We performed all the training and testing on the Tensorflow using the Keras library in Python using Google Colab. The developed approach proved its efficiency with 6 attributes attaining an accuracy of 98% for 150 epochs. The ANN model was also compared with existing work and attained a 10 fold cross validation accuracy of 96.26% with an increase of 1.2%.

Detection and Classification of Brain Tumors from MRI Images Using a Deep Convolutional Neural Network Approach

Brain tumor is a severe cancer disease caused by uncontrollable and abnormal partitioning of cells. Timely disease detection and treatment plans lead to the increased life expectancy of patients. Automated detection and classification of brain tumor are a more challenging process which is based on the clinician’s knowledge and experience. For this fact, one of the most practical and important techniques is to use deep learning. Recent progress in the fields of deep learning has helped the clinician’s in medical imaging for medical diagnosis of brain tumor. In this paper, we present a comparison of Deep Convolutional Neural Network models for automatically binary classification query MRI images dataset with the goal of taking precision tools to health professionals based on fined recent versions of DenseNet, Xception, NASNet-A, and VGGNet. The experiments were conducted using an MRI open dataset of 3,762 images. Other performance measures used in the study are the area under precision, recall, and specificity.

CLUSTER ANALYSIS IDENTIFIES VARIABLES RELATED TO PROGNOSIS OF BREAST CANCER DISEASE

This work presents a cluster analysis approach aiming to determine distinct groups based on clinicopathological data from patients with breast cancer (BC). For this purpose, the clinical variables were considered: age at diagnosis, weight, height, lymph nodal invasion (LN), tumor-node-metastasis (TNM) staging and body mass index (BMI). Ward's hierarchical clustering algorithm was used to form specific groups. Based on this, BC patients were separated into four groups. The Kruskal-Wallis test was performed to assess the differences among the clusters. The intensity of the influence of variables on the prognosis of BC was also evaluated by calculating the Spearman's correlation. Positive correlations were obtained between weight and BMI, TNM and LN invasion in all analyzes. Negative correlations between BMI and height were obtained in some of the analyzes. Finally, a new correlation was obtained, based on this approach, between weight and TNM, demonstrating that the trophic-adipose status of BC patients can be directly related to disease staging.

Cancer Diagnosis of Microscopic Biopsy Images Using a Social Spider Optimisation-Tuned Neural Network

One of the most dangerous diseases that threaten people is cancer. If diagnosed in earlier stages, cancer, with its life-threatening consequences, has the possibility of eradication. In addition, accuracy in prediction plays a significant role. Hence, developing a reliable model that contributes much towards the medical community in the early diagnosis of biopsy images with perfect accuracy comes to the forefront. This article aims to develop better predictive models using multivariate data and high-resolution diagnostic tools in clinical cancer research. This paper proposes the social spider optimisation (SSO) algorithm-tuned neural network to classify microscopic biopsy images of cancer. The significance of the proposed model relies on the effective tuning of the weights of the neural network classifier by the SSO algorithm. The performance of the proposed strategy is analysed with performance metrics such as accuracy, sensitivity, specificity, and MCC measures, and the attained results are 95.9181%, 94.2515%, 97.125%, and 97.68%, respectively, which shows the effectiveness of the proposed method for cancer disease diagnosis.

Epidemiology & Prognostication of Acute Myeloid Leukemia (AML) in India – A Brief Review

AML is represented by aggregation of ≥20% myeloid immature cells in the spongy marrow and most generally raise in the peripheral blood. A cytogenetic finding plays a vital role in the risk management and stratification of AML patients. AML is genetically and functionally a heterogenous malignant disease. In the western world leukemia is one of the most common among all cancers. India ranked 3rd in cancer disease after US and China. Management of AML is challenging specially for medium and low-income countries as it causes a huge economic burden to the patient and family. Molecular prognostic biomarkers will help in redefining the risk stratification more efficiently. Targeted drugs in pre-clinical and clinical trial recorded to have promising outcomes in AML. In this review we summarize the prevalence, incidence, and prognostication of AML.

The Effect of Terpenoid Natural Chinese Medicine Molecular Compound on Lung Cancer Treatment

Among all malignant tumors in the whole universe, the incidence and mortality of lung cancer disease rank first. Especially in the past few years, the occurrence of lung cancer in the urban population has continued to increase, which seriously threatens the lives and health of people. Among the many treatments for lung cancer, chemotherapy is the best one, but traditional chemotherapy has low specificity and drug resistance. To address the above issue, this study reviews the five biological pathways that common terpenoid compounds in medicinal plants interfere with the occurrence and development of lung cancer: cell proliferation, cell apoptosis, cell autophagy, cell invasion, metastasis, and immune mechanism regulation. In addition, the mechanism of the terpenoid natural traditional Chinese medicine monomer compound combined with Western medicine in the multipathway antilung cancer is summarized.

Undetectable Anti-HBs Antibodies: Need of a Booster Dose for HIV-1-Infected Individuals

HBV vaccination effectively prevents HBV transmission and the development of liver cancer. Disease progression and liver-related complications are more common in HIV-1/HBV co-infected than HBV mono-infected individuals. A considerable body of literature, which will be reviewed here, indicates that response to HBV vaccine is suboptimal in HIV-1-infected individuals and that the poor maintenance of protective immunity to HBV vaccines in these individuals is an important medical issue. Several factors affect HBV vaccine response during HIV-1 infection including CD4+ T cell counts, B cell response, vaccine formulation, schedules, and timing of antiretroviral therapy (ART). The initial response to HBV vaccination also plays a critical role in the sustainability of antibody responses in both HIV-1-infected and uninfected vaccinees. Thus, regular follow-up for antibody titer and a booster dose is warranted to prevent HBV transmission in HIV-1 infected people.

Familial Breast Cancer: Disease Related Gene Mutations and Screening Strategies for Chinese Population

BackgroundAbout 5%–10% of the breast cancer cases have a hereditary background, and this subset is referred to as familial breast cancer (FBC). In this review, we summarize the susceptibility genes and genetic syndromes associated with FBC and discuss the FBC screening and high-risk patient consulting strategies for the Chinese population.MethodsWe searched the PubMed database for articles published between January 2000 and August 2021. Finally, 380 pieces of literature addressing the genes and genetic syndromes related to FBC were included and reviewed.ResultsWe identified 16 FBC-related genes and divided them into three types (high-, medium-, and low-penetrance) of genes according to their relative risk ratios. In addition, six genetic syndromes were found to be associated with FBC. We then summarized the currently available screening strategies for FBC and discussed those available for high-risk Chinese populations.ConclusionMultiple gene mutations and genetic disorders are closely related to FBC. The National Comprehensive Cancer Network (NCCN) guidelines recommend corresponding screening strategies for these genetic diseases. However, such guidelines for the Chinese population are still lacking. For screening high-risk groups in the Chinese population, genetic testing is recommended after genetic counseling.