Hanahan and Weinberg introduced the “hallmarks of cancer” and typified essential biological abilities acquired by human cancer. Since then, a growing understanding of hallmark principles associated with breast cancer has assisted knowledge-based therapeutics development; however, despite the rapidly increasing number of targeted therapeutics, enduring disease-free responses for most forms of breast cancer is rare. Invasion and metastasis are the most defining feature of breast cancer malignancy and the leading cause of patient mortality. Hence, we propose a modified hallmarks model adapted to breast cancer, in which invasion and metastasis are shifted to the center of attention, thereby emphasizing it as a potentially superior therapeutic target. Although the scientific community highly appreciates the importance of the invasion and metastasis hallmark, as can be demonstrated by the growing number of publications on breast cancer metastasis, very few clinical trials concentrate on testing anti-metastasis inhibitors and even fewer trials focus on inhibitors for breast cancer metastasis. Here, we discuss the obstacles of applying research on invasion and metastasis therapeutics into the clinic and present current developments that could provide a potential solution to this dilemma.

Alzheimer’s disease (AD) is often comorbid with other pathologies. First, we review shortly the diseases most associated with AD in the clinic. Then we query PubMed citations for the co-occurrence of AD with other diseases, using a list of 400 common pathologies. Significantly, AD is found to be associated with schizophrenia and psychosis, sleep insomnia and apnea, type 2 diabetes, atherosclerosis, hypertension, cardiovascular diseases, obesity, fibrillation, osteoporosis, arthritis, glaucoma, metabolic syndrome, pain, herpes, HIV, alcoholism, heart failure, migraine, pneumonia, dyslipidemia, COPD and asthma, hearing loss, and tobacco smoking. Trivially, AD is also found to be associated with several neurodegenerative diseases, which are disregarded. Notably, our predicted results are consistent with the previously published clinical data and correlate nicely with individual publications. Our results emphasize risk factors and promulgate diseases often associated with AD. Interestingly, the comorbid diseases are often degenerative diseases exacerbated by reactive oxygen species, thus underlining the potential role of antioxidants in the treatment of AD and comorbid diseases.

Autoimmune diseases (AIDs) are often co-associated, and about 25% of patients with one AID tend to develop other comorbid AIDs. Here, we employ the power of datamining to predict the comorbidity of AIDs based on their normalized co-citation in PubMed. First, we validate our technique in a test dataset using earlier-reported comorbidities of seven knowns AIDs. Notably, the prediction correlates well with comorbidity (R = 0.91) and validates our methodology. Then, we predict the association of 100 AIDs and classify them using principal component analysis. Our results are helpful in classifying AIDs into one of the following systems: (1) gastrointestinal, (2) neuronal, (3) eye, (4) cutaneous, (5) musculoskeletal, (6) kidneys and lungs, (7) cardiovascular, (8) hematopoietic, (9) endocrine, and (10) multiple. Our classification agrees with experimentally based taxonomy and ranks AID according to affected systems and gender. Some AIDs are unclassified and do not associate well with other AIDs. Interestingly, Alzheimer’s disease correlates well with other AIDs such as multiple sclerosis. Finally, our results generate a network classification of autoimmune diseases based on PubMed text mining and help map this medical universe. Our results are expected to assist healthcare workers in diagnosing comorbidity in patients with an autoimmune disease, and to help researchers in identifying common genetic, environmental, and autoimmune mechanisms.

Arginases are often overexpressed in human diseases, and they are an important target for developing anti-aging and antineoplastic drugs. Arginase type 1 (ARG1) is a cytosolic enzyme, and arginase type 2 (ARG2) is a mitochondrial one. In this study, a dataset containing 2115-FDA-approved drug molecules is virtually screened for potential arginase binding using molecular docking against several ARG1 and ARG2 structures. The potential arginase ligands are classified into three categories: (1) Non-selective, (2) ARG1 selective, and (3) ARG2 selective. The evaluated potential arginase ligands are then compared with their clinical use. Remarkably, half of the top 30 potential drugs are used clinically to lower blood pressure and treat cancer, infection, kidney disease, and Parkinson’s disease thus partially validating our virtual screen. Most notable are the antihypertensive drugs candesartan, irbesartan, indapamide, and amiloride, the antiemetic rolapitant, the anti-angina ivabradine, and the antidiabetic metformin which have minimal side effects. The partial validation also favors the idea that the other half of the top 30 potential drugs could be used in therapeutic settings. The three categories greatly expand the selectivity of arginase inhibition.

Breast cancer is the second leading cause of morbidity and mortality in women worldwide. Despite advancements in the clinical application of neoadjuvant chemotherapy (NAC), drug resistance remains a major concern hindering treatment efficacy. Thus, identifying the key genes involved in driving NAC resistance and targeting them with known potential FDA-approved drugs could be applied to advance the precision medicine strategy. With this aim, we performed an integrative bioinformatics study to identify the key genes associated with NAC resistance in breast cancer and then performed the drug repurposing to identify the potential drugs which could use in combination with NAC to overcome drug resistance. In this study, we used publicly available RNA-seq datasets from the samples of breast cancer patients sensitive and resistant to chemotherapy and identified a total of 1446 differentially expressed genes in NAC-resistant breast cancer patients. Next, we performed gene co-expression network analysis to identify significantly co-expressed gene modules, followed by MCC (Multiple Correlation Clustering) clustering algorithms and identified 33 key hub genes associated with NAC resistance. mRNA–miRNA network analysis highlighted the potential impact of these hub genes in altering the regulatory network in NAC-resistance breast cancer cells. Further, several hub genes were found to be significantly involved in the poor overall survival of breast cancer patients. Finally, we identified FDA-approved drugs which could be useful for potential drug repurposing against those hub genes. Altogether, our findings provide new insight into the molecular mechanisms of NAC resistance and pave the way for drug repurposing techniques and personalized treatment to overcome NAC resistance in breast cancer.

Metastasis accounts for the majority of cancer-related deaths. Despite decades of research, the prevention and suppression of metastasis remain an elusive goal, and to date, only a few metastasis-related genes have been targeted therapeutically. Thus, there is a strong need to find potential genes involved in key driver traits of metastasis and their available drugs. In this study, we identified genes associated with metastasis and repurposable drugs that potentially target them. First, we use text mining of PubMed citations to identify candidate genes associated with metastatic processes, such as invadopodia, motility, movement, metastasis, invasion, wound healing, EMT (epithelial to mesenchymal transition), and podosome. Next, we annotated the top genes involved in each process as a driver, tumor suppressor, or oncogene. Then, a total of 185 unique cancer genes involved in metastasis-related processes were used for hub gene analysis using bioinformatics tools. Notably, a total of 77 hub genes were identified. Further, we used virtual screening data of druggable candidate hub genes involved in metastasis and identified potential drugs that can be repurposed as anti-metastatic drugs. Remarkably, we found a total of 50 approved drugs that have the potential to be repurposed against 19 hub genes involved in metastasis-related processes. These 50 drugs were also found to be validated in different cancer cell lines, such as dasatinib, captopril, leflunomide, and dextromethorphan targeting SRC, MMP2, PTK2B, and RAC1 hub genes, respectively. These repurposed drugs potentially target metastasis, provide pharmacodynamic insight, and offer a window of opportunity for the development of much-needed antimetastatic drugs.