The gut microbiota significantly impacts human health, influencing metabolism, immunological responses, and disease prevention. Dysbiosis, or microbial imbalance, is linked to various diseases, including cancer. It is crucial to preserve a healthy microbiome since pathogenic bacteria, such as Escherichia coli and Fusobacterium nucleatum, can cause inflammation and cancer. These pathways can lead to the formation of tumors. Recent advancements in high-throughput sequencing, metagenomics, and machine learning have revolutionized our understanding of the role of gut microbiota in cancer risk prediction. Early detection is made easier by machine learning algorithms that improve the categorization of cancer kinds based on microbiological data. Additionally, the investigation of the microbiome has been transformed by next-generation sequencing (NGS), which has made it possible to fully profile both cultivable and non-cultivable bacteria and to understand their roles in connection with cancer. Among the uses of NGS are the detection of microbial fingerprints connected to treatment results and the investigation of metabolic pathways implicated in the development of cancer. The combination of NGS with machine learning opens up new possibilities for creating customized medicine by enabling the development of diagnostic tools and treatments that are specific to each patient’s microbiome profile, even in the face of obstacles like data complexity. Multi-omics studies reveal microbial interactions, biomarkers for cancer detection, and gut microbiota’s impact on cancer progression, underscoring the need for further research on microbiome-based cancer prevention and therapy.

Asthma is a chronic inflammatory disease with no definite treatment and more research is needed to overcome this condition. The aim of this study was to investigate the effect of the extract of Zataria multiflora (Z. multiflora) as a medicinal plant on cytokine genes expression in an experimental mouse model of asthma. Adult mice were randomly divided into the following groups: control (C), untreated asthma (A), asthmatic groups treated with dexamethasone (D) and Z. multiflora extract (200,400 and 800 µg/mL) (Z1, Z2, and Z3, respectively), (for groups C, A and D n=5 and for groups Z1, Z2 and Z3 n=6). For induction of the mouse model of asthma, animals were sensitized with intraperitoneal injection and inhalation of ovalbumin (OVA). The number of T helper (Th) subtype cells (using flow cytometry) and the levels of IFN-γ, FOXP3, IL-4, TGF-β, IL-17 gene expression (by real time PCR) were assessed in mice splenocytes. The observed changes in spleen cells of group A compared to group C were increased number of Th2 and Th17 cells, enhancement of gene expression of IL-4, IL-17 and TGF-β (p<0.001 for all cases), reduction of Th1 cells and Th1/Th2 ratio (p<0.001 for both cases) and decrease in gene expression of IFN-γ, FOXP3 and IFN-γ/IL-4 ratio (p<0.01 for IFN-γ and p<0.001 for other cases). The observed changes in spleen cells of treated compared to untreated A group were enhancement of Treg cells and Th1/Th2 ratio (p<0.001 for both cases), increase in IFN-γ (p<0.05) and FOXP3 (p<0.001) gene expression and IFN-γ/IL-4 ratio (p<0.01) as well as reduction of Th2 and Th17 cells (p<0.01 to p<0.001), decrease gene expression of IL-4, IL-17 and TGF-β (p<0.05 to p<0.001). The findings showed that the extract of Z. multiflora decreased pro-inflammatory cytokines in asthma (IL-4 and IL-17 and TGF-β) but increased anti-inflammatory cytokines (IFN-γ) gene expression and the number of Treg (FOXP3) in splenocytes of asthmatic mice which may indicate the specific therapeutic effect of the plant extract in allergy, autoimmunity and infectious diseases via potentiating Th1 and suppressing Th2 and Th17 cells.