Single-Nucleotide Polymorphisms Promote Dysregulation Activation by Essential Gene Mediated Bio-Molecular Interaction in Breast Cancer

Knockdown of ABHD11‑AS1 prevents the procession of TNBC by upregulating miR‑199a‑5p

Breast cancer (BC) has become a threat to women's health. In addition, patients with triple-negative BC (TNBC) have the worst prognosis among all patients with BC. Furthermore, long non-coding RNA ABHD11-AS1 is aberrantly highly expressed in TNBC, suggesting that RNA ABHD11-AS1 may serve as an important role in the progression of TNBC. However, the detailed function of ABHD11-AS1 in TNBC remains largely unknown. The levels of ABHD11-AS1 in MDA-MB-231 cells were assessed by reverse transcription-quantitative PCR. To investigate the effect of ABHD11-AS1 on the progression of TNBC, a xenograft animal model was established. Knockdown of ABHD11-AS1 inhibited the epithelial-mesenchymal transition and migration of TNBC cells. In addition, ABHD11-AS1 promoted the viability and migration of TNBC cells by upregulating microRNA (miR)-199a-5p. Furthermore, knockdown of ABHD11-AS1 suppressed TNBC tumor growth in vivo by upregulating miR-199a-5p. In conclusion, knockdown of ABHD11-AS1 suppressed the progression of TNBC via upregulation of miR-199a-5p. The data of the present study may provide novel directions and a theoretical basis for TNBC treatment.

A Survey on the Role of Artificial Intelligence in Biobanking Studies: A Systematic Review

Introduction: In biobanks, participants' biological samples are stored for future research. The application of artificial intelligence (AI) involves the analysis of data and the prediction of any pathological outcomes. In AI, models are used to diagnose diseases as well as classify and predict disease risks. Our research analyzed AI's role in the development of biobanks in the healthcare industry, systematically. Methods: The literature search was conducted using three digital reference databases, namely PubMed, CINAHL, and WoS. Guidelines for preferred reporting elements for systematic reviews and meta-analyses (PRISMA)-2020 in conducting the systematic review were followed. The search terms included "biobanks", "AI", "machine learning", and "deep learning", as well as combinations such as "biobanks with AI", "deep learning in the biobanking field", and "recent advances in biobanking". Only English-language papers were included in the study, and to assess the quality of selected works, the Newcastle-Ottawa scale (NOS) was used. The good quality range (NOS ≥ 7) is only considered for further review. Results: A literature analysis of the above entries resulted in 239 studies. Based on their relevance to the study's goal, research characteristics, and NOS criteria, we included 18 articles for reviewing. In the last decade, biobanks and artificial intelligence have had a relatively large impact on the medical system. Interestingly, UK biobanks account for the highest percentage of high-quality works, followed by Qatar, South Korea, Singapore, Japan, and Denmark. Conclusions: Translational bioinformatics probably represent a future leader in precision medicine. AI and machine learning applications to biobanking research may contribute to the development of biobanks for the utility of health services and citizens.

ABHD11-AS1: An Emerging Long Non-Coding RNA (lncRNA) with Clinical Significance in Human Malignancies

The aberrant expression of lncRNAs has been linked to the development and progression of different cancers. One such lncRNA is ABHD11 antisense RNA 1 (ABHD11-AS1), which has recently gained attention for its significant role in human malignancies. ABHD11-AS1 is highly expressed in gastric, lung, breast, colorectal, thyroid, pancreas, ovary, endometrium, cervix, and bladder cancers. Several reports highlighted the clinical significance of ABHD11-AS1 in prognosis, diagnosis, prediction of cancer progression stage, and treatment response. Significantly, the levels of ABHD11-AS1 in gastric juice had been exhibited as a clinical biomarker for the assessment of gastric cancer, while its serum levels have prognostic potential in thyroid cancers. The ABHD11-AS1 has been reported to exert oncogenic effects by sponging different microRNAs (miRNAs), altering signaling pathways such as PI3K/Akt, epigenetic mechanisms, and N6-methyladenosine (m⁶A) RNA modification. In contrast, the mouse homolog of AHD11-AS1 (Abhd11os) overexpression had exhibited neuroprotective effects against mutant huntingtin-induced toxicity. Considering the emerging research reports, the authors attempted in this first review on ABHD11-AS1 to summarize and highlight its oncogenic potential and clinical significance in different human cancers. Lastly, we underlined the necessity for future mechanistic studies to unravel the role of ABHD11-AS1 in tumor development, prognosis, progression, and targeted therapeutic approaches.