Machine Learning Methods for Survival Analysis with Clinical and Transcriptomics Data of Breast Cancer

Identifying Effective Biomarkers for Accurate Pancreatic Cancer Prognosis Using Statistical Machine Learning

Pancreatic cancer (PC) has one of the lowest survival rates among all major types of cancer. Consequently, it is one of the leading causes of mortality worldwide. Serum biomarkers historically correlate well with the early prognosis of post-surgical complications of PC. However, attempts to identify an effective biomarker panel for the successful prognosis of PC were almost non-existent in the current literature. The current study investigated the roles of various serum biomarkers including carbohydrate antigen 19-9 (CA19-9), chemokine (C-X-C motif) ligand 8 (CXCL-8), procalcitonin (PCT), and other relevant clinical data for identifying PC progression, classified into sepsis, recurrence, and other post-surgical complications, among PC patients. The most relevant biochemical and clinical markers for PC prognosis were identified using a random-forest-powered feature elimination method. Using this informative biomarker panel, the selected machine-learning (ML) classification models demonstrated highly accurate results for classifying PC patients into three complication groups on independent test data. The superiority of the combined biomarker panel (Max AUC-ROC = 100%) was further established over using CA19-9 features exclusively (Max AUC-ROC = 75%) for the task of classifying PC progression. This novel study demonstrates the effectiveness of the combined biomarker panel in successfully diagnosing PC progression and other relevant complications among Egyptian PC survivors.

Ten quick tips for avoiding pitfalls in multi-omics data integration analyses

Data are the most important elements of bioinformatics: Computational analysis of bioinformatics data, in fact, can help researchers infer new knowledge about biology, chemistry, biophysics, and sometimes even medicine, influencing treatments and therapies for patients. Bioinformatics and high-throughput biological data coming from different sources can even be more helpful, because each of these different data chunks can provide alternative, complementary information about a specific biological phenomenon, similar to multiple photos of the same subject taken from different angles. In this context, the integration of bioinformatics and high-throughput biological data gets a pivotal role in running a successful bioinformatics study. In the last decades, data originating from proteomics, metabolomics, metagenomics, phenomics, transcriptomics, and epigenomics have been labelled -omics data, as a unique name to refer to them, and the integration of these omics data has gained importance in all biological areas. Even if this omics data integration is useful and relevant, due to its heterogeneity, it is not uncommon to make mistakes during the integration phases. We therefore decided to present these ten quick tips to perform an omics data integration correctly, avoiding common mistakes we experienced or noticed in published studies in the past. Even if we designed our ten guidelines for beginners, by using a simple language that (we hope) can be understood by anyone, we believe our ten recommendations should be taken into account by all the bioinformaticians performing omics data integration, including experts.

The Effects of Omega-3 Polyunsaturated Fatty Acids on Breast Cancer as a Preventive Measure or as an Adjunct to Conventional Treatments

In order to understand how omega-3 polyunsaturated fatty acid (ω-3 PUFA) supplements affect breast cancer prevention and treatment, a systematic review of articles published in the last 5 years in two databases was performed. Of the 679 articles identified, only 27 were included and examined based on five topics, taking into account: the induction type of the breast cancer used in animal models; the characteristics of the induction model by cell transplantation; the experimental design of the ω-3 supplementation—combined or not with a treatment antitumor drug; the fatty acids (FAs) composition used; the analysis of the studies’ outcomes. There are diverse and well-established animal models of breast cancer in the literature, with very relevant histological and molecular similarities depending on the specific objective of the study, such as whether the method of tumor induction was transgenic, by cell transplantation, or by oncogenic drugs. The analyses of outcomes were mainly focused on monitoring tumor growth, body/tumor weight, and molecular, genetic, or histological analyses, and few studies evaluated latency, survival, or metastases. The best results occurred when supplementation with ω-3 PUFA was associated with antitumor drugs, especially in the analysis of metastases and volume/weight of tumors or when the supplementation was started early and maintained for a long time. However, the beneficial effect of ω-3 PUFA supplementation when not associated with an antitumor agent remains unclear.