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Oral cancer is one of the 19most rapidly progressing cancers associated with significant mortality, owing to its extreme degree of invasiveness and aggressive inclination. The early occurrences of this cancer can be clinically deceiving leading to a poor overall survival rate. The primary concerns from a clinical perspective include delayed diagnosis, rapid disease progression, resistance to various chemotherapeutic regimens, and aggressive metastasis, which collectively pose a substantial threat to prognosis. Conventional clinical practices observed since antiquity no longer offer the best possible options to circumvent these roadblocks. The world of current cancer research has been revolutionized with the advent of state-of-the-art technology-driven strategies that offer a ray of hope in confronting said challenges by highlighting the crucial underlying molecular mechanisms and drivers. In recent years, bioinformatics and Machine Learning (ML) techniques have enhanced the possibility of early detection, evaluation of prognosis, and individualization of therapy. This review elaborates on the application of the aforesaid techniques in unraveling potential hints from omics big data to address the complexities existing in various clinical facets of oral cancer. The first section demonstrates the utilization of omics data and ML to disentangle the impediments related to diagnosis. This includes the application of technology-based strategies to optimize early detection, classification, and staging via uncovering biomarkers and molecular signatures. Furthermore, breakthrough concepts such as salivaomics-driven non-invasive biomarker discovery and omics-complemented surgical interventions are articulated in detail. In the following part, the identification of novel diseasespecific targets alongside potential therapeutic agents to confront oral cancer via omics-based methodologies is presented. Additionally, a special emphasis is placed on drug resistance, precision medicine, and drug repurposing. In the final section, we discuss the research approaches oriented toward unveiling the prognostic biomarkers and constructing prediction models to capture the metastatic potential of the tumors. Overall, we intend to provide a bird's eye view of the various omics, bioinformatics, and ML approaches currently being used in oral cancer research through relevant case studies. [ABSTRACT FROM AUTHOR]

Background: Elucidation of genetic associations underlying Alzheimer's Disease (AD) pathogenesis and progression is of prime importance in early diagnosis and drug discovery. Despite existence of innumerable studies to comprehend the genetic hallmarks of AD, inconsistencies amongst the studies in ascertaining specific genes involved in AD risk prompted this Systematic Review and Meta‐analysis. Method: The review protocol was registered in Prospero (CRD42019127482). AD related Boolean search strategy was generated to retrieve case‐control studies evaluating genetic associations in AD patients from Pubmed/MEDLINE, Cochrane library, Proquest, Europe PMC, Grey literature, HuGE navigator, Latin American and Carribean Health Sciences literature published till March‐2019. Case‐control studies that defined AD diagnosis through standard diagnostic criteria were included. Preclinical and in‐silico studies were excluded. The shortlisted studies were critically appraised through New Castle Ottawa Scale (NOS) and Q‐Genie tool and the resultant data was extracted. Meta‐analysis was performed for Single Nucleotide Polymorphisms (SNPs) that were reported to be replicated in two different ethnicities by at‐least two studies through random effects model using Revman 5.3. Publication bias was assessed using Egger's test, funnel plot, Begg and Mazumdar rank correlation test. Later, Interim Venice assessment and sensitivity analysis were executed to evaluate the credibility and versatility of the selected studies respectively. Result: Among 352 285 studies retrieved, only 793 studies that met the eligibility criteria were critically appraised. This ultimately resulted in 118 studies for systematic review out of which, 23 SNPs corresponding to 15 genes were prioritized for meta‐analysis. The following SNPs were found to be significantly associated with AD risk: rs3865444 (CD33) (p = 0.04; I2 = 40%; OR [CI] = 0.88 [0.78‐0.99]), rs7561528 (BIN1) (p = 0.03; I2 = 46%; OR [CI] = 0.86 [0.76‐0.98]) and rs1801133 (MTHFR) (p = 0.007; I2 = 18%; OR [CI] = 0.73 [0.61‐0.88]). Interim Venice criteria revealed moderate credibility for seven SNPs and weak credibility for 16 SNPs. Further, sensitivity analysis confirmed the versatility of the selected studies. Conclusion: Our findings acknowledged significant associations of three SNPs: rs3865444, rs7561528 and rs1801133 with Alzheimer's risk. This evidence has to be replicated further to substantiate the role of aforesaid SNPs in AD. [ABSTRACT FROM AUTHOR]

• 2777 case-control studies were critically appraised through NOS and Q-Genie scales. • 117 case-control studies belonging to 14 ethnicities were systematically reviewed. • 23 SNPs belonging to 15 genes were meta-analysed. • SNPs belonging to CD33, BIN1 and MTHFR exhibited significant AD risk. • SNPs of SIRT2, MAPT, ABCA7, TOMM40, CLU, PICALM, TTBK1 & CYP46A1 displayed AD protection. Drug discovery for Alzheimer's Disease (AD) is channeled towards unravelling key disease specific drug targets/genes to predict promising therapeutic candidates. Though enormous literature on AD genetics is available, there exists dearth in data pertinent to drug targets and crucial pathological pathways intertwined in disease progression. Further, the research findings revealing genetic associations failed to demonstrate consistency across different studies. This scenario prompted us to initiate a systematic review and meta-analysis with an aim of unearthing significant genetic hallmarks of AD. Initially, a Boolean search strategy was developed to retrieve case-control studies from PubMed, Cochrane, ProQuest, Europe PMC, grey literature and HuGE navigator. Subsequently, certain inclusion and exclusion criteria were framed to shortlist the relevant studies. These studies were later critically appraised using New Castle Ottawa Scale and Q-Genie followed by data extraction. Later, meta-analysis was performed only for those Single Nucleotide Polymorphisms (SNPs) which were evaluated in at least two different ethnicities from two different reports. Among, 204,351 studies retrieved, 820 met our eligibility criteria and 117 were processed for systematic review after critical appraisal. Ultimately, meta-analysis was performed for 23 SNPs associated with 15 genes which revealed significant associations of rs3865444 (CD33), rs7561528 (BIN1) and rs1801133 (MTHFR) with AD risk. [ABSTRACT FROM AUTHOR]

Female cancers, which include breast and gynaecological cancers, represent a significant global health burden for women. Despite advancements in research pertinent to unearthing crucial pathological characteristics of these cancers, challenges persist in discovering potential therapeutic strategies. This is further exacerbated by economic burdens associated with de novo drug discovery and clinical intricacies such as development of drug resistance and metastasis. Drug repurposing, an innovative approach leveraging existing FDA-approved drugs for new indications, presents a promising avenue to expedite therapeutic development. Computational techniques, including virtual screening and analysis of drug-target-disease relationships, enable the identification of potential candidate drugs. Integration of diverse data types, such as omics and clinical information, enhances the precision and efficacy of drug repurposing strategies. Experimental approaches, including high-throughput screening assays, in vitro, and in vivo models, complement computational methods, facilitating the validation of repurposed drugs. This review highlights various target mining strategies based on analysis of differential gene expression, weighted gene co-expression, protein-protein interaction network, and host-pathogen interaction, among others. To unearth drug candidates, the technicalities of leveraging information from databases such as DrugBank, STITCH, LINCS, and ChEMBL, among others are discussed. Further in silico validation techniques encompassing molecular docking, pharmacophore modelling, molecular dynamic simulations, and ADMET analysis are elaborated. Overall, this review delves into the exploration of individual case studies to offer a wide perspective of the ever-evolving field of drug repurposing, emphasizing the multifaceted approaches and methodologies employed for the same to confront female cancers., (Copyright © 2024. Published by Elsevier Inc.)

The conventional theory linking a single gene with a particular disease and a specific drug contributes to the dwindling success rates of traditional drug discovery. This requires a substantial shift focussing on contemporary drug design or drug repurposing, which entails linking multiple genes to diverse physiological or pathological pathways and drugs. Lately, drug repurposing, the art of discovering new/unlabelled indications for existing drugs or candidates in clinical trials, is gaining attention owing to its success rates. The rate-limiting phase of this strategy lies in target identification, which is generally driven through disease-centric and/or drug-centric approaches. The disease-centric approach is based on exploration of crucial biomolecules such as genes or proteins underlying pathological cascades of the disease of interest. Investigating these pathological interplays aids in the identification of potential drug targets that can be leveraged for novel therapeutic interventions. The drug-centric approach involves various strategies such as exploring the mechanism of adverse drug reactions that can unearth potential targets, as these untoward reactions might be considered desirable therapeutic actions in other disease conditions. Currently, artificial intelligence is an emerging robust tool that can be used to translate the aforementioned intricate biological networks to render interpretable data for extracting precise molecular targets. Integration of multiple approaches, big data analytics, and clinical corroboration are essential for successful target mining. This chapter highlights the contemporary strategies steering target identification and diverse frameworks for drug repurposing. These strategies are illustrated through case studies curated from recent drug repurposing research inclined towards neurodegenerative diseases, cancer, infections, immunological, and cardiovascular disorders., (Copyright © 2024. Published by Elsevier Inc.)

The clinical management of oral cancer is often frequented with challenges that arise from relapse, recurrence, invasion and resistance towards the cornerstone chemo and radiation therapies. The recent conceptual advancement in oncology has substantiated the role of cancer stem cells (CSC) as a predominant player of these intricacies. CSC are a sub-group of tumor population with inherent adroitness to self-renew with high plasticity. During tumor evolution, the structural and functional reprogramming persuades the cancer cells to acquire stem-cell like properties, thus presenting them with higher survival abilities and treatment resistance. An appraisal on key features that govern the stemness is of prime importance to confront the current challenges encountered in oral cancer. The nurturing niche of CSC for maintaining its stemness characteristics is thought to be modulated by complex multi-layered components encompassing neoplastic cells, extracellular matrix, acellular components, circulatory vessels, various cascading signaling molecules and stromal cells. This review focuses on recapitulating both intrinsic and extrinsic mechanisms that impart the stemness. There are contemplating evidences that demonstrate the role of transcription factors (TF) in sustaining the neoplastic stem cell's pluripotency and plasticity alongside the miRNA in regulation of crucial genes involved in the transformation of normal oral mucosa to malignancy. This review illustrates the interplay between miRNA and various known TF of oral cancer such as c-Myc, SOX, STAT, NANOG and OCT in orchestrating the stemness and resistance features. Further, the cross-talks involved in tumor micro-environment inclusive of cytokines, macrophages, extra cellular matrix, angiogenesis leading pathways and influential factors of hypoxia on tumorigenesis and CSC survival have been elucidated. Finally, external factorial influence of oral microbiome gained due to the dysbiosis is also emphasized. There are growing confirmations of the possible roles of microbiomes in the progression of oral cancer. Given this, an attempt has been made to explore the potential links including EMT and signaling pathways towards resistance and stemness. This review provides a spectrum of understanding on stemness and progression of oral cancers at various regulatory levels along with their current therapeutic knowledge. These mechanisms could be exploited for future research to expand potential treatment strategies., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Vastrad, Ritesh, V, Saraswathy, Augustine, Alzahrani, Alzahrani, Halawani, Ashi, Alshahrani, Hassan, Baeshen, Saravanan, Satish, Vutukuru and Patil.)