Gene expression trend changes in breast cancer populations over two decades: insights from The Cancer Genome Atlas database

Adaptive Sparse Multi-Block PLS Discriminant Analysis: An Integrative Method for Identifying Key Biomarkers from Multi-Omics Data

With the growing use of high-throughput technologies, multi-omics data containing various types of high-dimensional omics data is increasingly being generated to explore the association between the molecular mechanism of the host and diseases. In this study, we present an adaptive sparse multi-block partial least square discriminant analysis (asmbPLS-DA), an extension of our previous work, asmbPLS. This integrative approach identifies the most relevant features across different types of omics data while discriminating multiple disease outcome groups. We used simulation data with various scenarios and a real dataset from the TCGA project to demonstrate that asmbPLS-DA can identify key biomarkers from each type of omics data with better biological relevance than existing competitive methods. Moreover, asmbPLS-DA showed comparable performance in the classification of subjects in terms of disease status or phenotypes using integrated multi-omics molecular profiles, especially when combined with other classification algorithms, such as linear discriminant analysis and random forest. We have made the R package called asmbPLS that implements this method publicly available on GitHub. Overall, asmbPLS-DA achieved competitive performance in terms of feature selection and classification. We believe that asmbPLS-DA can be a valuable tool for multi-omics research.

EBV and Lymphomagenesis

The clinical significance of Epstein–Barr virus (EBV) cannot be understated. Not only does it infect approximately 90% of the world’s population, but it is also associated with numerous pathologies. Diseases linked to this virus include hematologic malignancies such as diffuse large B-cell lymphoma, Hodgkin lymphoma, Burkitt lymphoma, primary CNS lymphoma, and NK/T-cell lymphoma, epithelial malignancies such as nasopharyngeal carcinoma and gastric cancer, autoimmune diseases such as multiple sclerosis, Graves’ disease, and lupus. While treatment for these disease states is ever evolving, much work remains to more fully elucidate the relationship between EBV, its associated disease states, and their treatments. This paper begins with an overview of EBV latency and latency-associated proteins. It will then review EBV’s contributions to select hematologic malignancies with a focus on the contribution of latent proteins as well as their associated management.

Knowledge and attitudes relating to cervical and breast cancer among women in Maseru, Lesotho

BACKGROUND

Cancer has remained one of the leading causes of death worldwide. In Lesotho, breast and cervical cancers contribute about 43% of all the cancer cases annually.

AIM

This study is aimed at comparing knowledge, attitudes, and practices between breast and cervical cancers among females in Maseru.

SETTINGS

This study consists of women residing in five study sites which have clinics that offer cervical and breast cancer-screening services.

METHODS

A cross-sectional study was conducted in June 2021 in Maseru, the Capital city of Lesotho. The participants were interviewed using a pre-tested questionnaire, through which their knowledge, practices about, and attitudes towards breast and cervical cancers were assessed.

RESULTS

A total of 228 women aged 15-75 years participated in the study and the majority were aged 30 years and above. Of the women interviewed for cervical cancer, 89.5% had heard of it, 11.8% had heard of its screening, and 7.4% had at least one examination. Similarly, for breast cancer, 77.6% of women who had heard of it, 72.9% had heard of screening, and 40.1% of women did at least one examination.

CONCLUSION

The majority of women were more knowledgeable about cervical cancer than breast cancer. However, more women had heard about breast cancer screening than cervical cancer screening. Therefore, there is a need for awareness campaigns related to cervical cancers' screening.Contribution: There is an urgent need to intensify awareness about cervical and breast cancer screening and availability of services at the nearby clinics.

A novel risk model based on cuproptosis-related lncRNAs predicted prognosis and indicated immune microenvironment landscape of patients with cutaneous melanoma

Cutaneous melanoma (CM) is an aggressive form of malignancy with poor prognostic value. Cuproptosis is a novel type of cell death regulatory mechanism in tumors. However, the role of cuproptosis-related long noncoding RNAs (lncRNAs) in CM remains elusive. The cuproptosis-related lncRNAs were identified using the Pearson correlation algorithm. Through the univariate and multivariate Cox regression analysis, the prognosis of seven lncRNAs associated with cuproptosis was established and a new risk model was constructed. ESTIMATE, CIBERSORT, and single sample gene set enrichment analyses (ssGSEA) were applied to evaluate the immune microenvironment landscape. The Kaplan–Meier survival analysis revealed that the overall survival (OS) of CM patients in the high-risk group was remarkably lower than that of the low-risk group. The result of the validated cohort and the training cohort indicated that the risk model could produce an accurate prediction of the prognosis of CM. The nomogram result demonstrated that the risk score based on the seven prognostic cuproptosis-related lncRNAs was an independent prognostic indicator feature that distinguished it from other clinical features. The result of the immune microenvironment landscape indicated that the low-risk group showed better immunity than high-risk group. The immunophenoscore (IPS) and immune checkpoints results conveyed a better benefit potential for immunotherapy clinical application in the low-risk groups. The enrichment analysis and the gene set variation analysis (GSVA) were adopted to reveal the role of cuproptosis-related lncRNAs mediated by the immune-related signaling pathways in the development of CM. Altogether, the construction of the risk model based on cuproptosis-related lncRNAs can accurately predict the prognosis of CM and indicate the immune microenvironment of CM, providing a new perspective for the future clinical treatment of CM.